Python Overview
Python will be a general-purpose programming language with many excellent features, such as being easy to learn, easy to write readable code, and usable for a wide range of applications Python was developed by Guido van Rossum in 1991.
Because Python is a relatively new language, it can utilize a variety of effective programming techniques such as object-oriented programming, procedural programming, and functional programming. It is also widely used in web applications, desktop applications, scientific and technical computing, machine learning, artificial intelligence, and other fields because of the many libraries and frameworks available. Furthermore, Python is cross-platform and runs on many operating systems, including Windows, Mac, and Linux, etc. Because Python is an interpreted language, it does not require compilation and has a REPL-like structure, which speeds up the development cycle.
The following development environments are available for Python
- Anaconda: Anaconda is an all-in-one data science platform that includes the necessary packages and libraries for data science in Python, as well as tools such as Jupyter Notebook to easily start data analysis and machine learning projects. It will also include tools such as Jupyter Notebook to make it easy to get started with data analysis and machine learning projects.
- PyCharm: PyCharm is a Python integrated development environment (IDE) developed by JetBrains that provides many features necessary for Python development, such as debugging, auto-completion, testing, project management, and version control to improve the quality and productivity of your projects. It is designed to improve the quality and productivity of your projects.
- Visual Studio Code: Visual Studio Code is an open source code editor developed by Microsoft that also supports Python development. It has a rich set of extensions that make it easy to add the functionality needed for Python development.
- IDLE: IDLE is a simple, easy-to-use, standard development environment that comes with Python and is ideal for learning Python.
These environments will be used to implement web applications and machine learning code. frameworks for web applications will provide many of the features needed for web application development, such as functionality based on the MVC architecture, security, databases, authentication, etc. The following are some of the most common
- Django: Django is one of the most widely used web application frameworks in Python, allowing the development of fast and robust applications based on the MVC architecture.
- Flask: Flask is a lightweight and flexible web application framework with a lower learning cost than Django, and is used by both beginners and advanced programmers.
- Pyramid: Pyramid is a web application framework with a flexible architecture and rich feature set that is more highly customizable than Django or Flask, making it suitable for large-scale applications.
- Bottle: Bottle is a lightweight and simple web application framework that makes it easy to build small applications and APIs.
Finally, here are some libraries for dealing with machine learning.
- Scikit-learn: Scikit-learn is the most widely used machine learning library in Python. It offers a variety of machine learning algorithms, including classification, regression, clustering, and dimensionality reduction.
- TensorFlow: TensorFlow is an open source machine learning library developed by Google that provides many features for building, training, and inference of neural networks.
- PyTorch: PyTorch is an open source machine learning library developed by Facebook that provides many of the same features as TensorFlow, including neural network construction, training, and inference.
- Keras: Keras is a library that provides a high-level neural network API and supports TensorFlow, Theano, and Microsoft Cognitive Toolkit backends.
- Pandas: Pandas is a library for data processing and can handle tabular data. In machine learning, it is often used for data preprocessing.
Various applications can be built by successfully combining these libraries and frameworks.
Python and Machine Learning
Python is a high-level language that is programmed using abstract instructions given by the designer (synonyms include low-level, which is programmed at the machine level using instructions and data objects), a general-purpose language that can be applied to a variety of purposes (synonyms include ), general-purpose languages that can be applied to a variety of applications (synonyms include targted to an application, in which the language is optimized for a specific use), and source code, in which the instructions written by the programmer are executed directly (by the interpreter) (synonyms include ) into basic machine-level instructions first.
Python is a versatile programming language that can be used to create almost any program efficiently without the need for direct access to computer hardware, and is not suitable for programs that require a high level of reliability (due to weak checks on static semantics). Python is not suitable for programs that require high reliability (due to weak checks on static semantics), nor (for the same reason) for programs that involve a large number of people or are developed and maintained over a long period of time.
However, Python is a relatively simple language that is easy to learn, and because it is designed as an interpreted language, it provides immediate feedback, which is very useful for novice programmers. It also has a number of freely available libraries that can be used to extend the language.
Python was developed by Guido von Rossum in 1990, and for the first decade it was a little-known and rarely used language, but Python 2.0 in 2000 marked a shift in the evolutionary path with a number of important improvements to the language itself. In 2008, Python 3.0 was released. In 2008, Python 3.0 was released. This version of Python improved many inconsistencies in Python 2. In 2008, Python 3.0 was released. This version of Python improved many inconsistencies of Python 2, but it was not backward compatible (most programs written in previous versions of Python would not work).
In the last few years, most of the important public domain Python libraries have been ported to Python3 and are being used by many more people.
In this blog, we discuss the following topics related to Python.
General Implementation
- Overview of Code as Data and Examples of Algorithms and Implementations
“Code as Data” refers to a concept or approach that treats the code of a program itself as data, and is a method that allows programs to be manipulated, analyzed, transformed, and processed as data structures. Normally, a program receives an input, executes a specific procedure or algorithm on it, and outputs the result. In “Code as Data,” on the other hand, the program itself is treated as data and manipulated by other programs. This allows programs to be handled more flexibly, dynamically, and abstractly.
In order to program, it is necessary to create a development environment for each language. This section describes how to set up specific development environments for Python, Clojure, C, Java, R, LISP, Prolog, Javascript, and PHP, as described in this blog. Each language has its own platform to facilitate development, which makes it possible to easily set up the environment, but this section focuses on the simplest case.
This section describes how to set up a Python development environment with SublimeText4 and VS code.
Before discussing Python, I will discuss programming and computers.
Computers do two things (and only two things). One is to perform calculations, and the other is to remember the results of calculations. However, computers are very good at both of these things. Even an ordinary computer performs about one billion calculations per second. The several hundred gigabytes of capacity of a typical computer is equivalent to the weight of several hundred thousand tons or more, or tens of thousands of African elephants, if we imagine it at 1 g per byte, for example.
Now consider “computational thinking” for solving problems computationally. All knowledge can be classified as either declarative or imperative. Declarative knowledge consists of statements of fact, while imperative knowledge is “how-to” knowledge, a recipe for deriving information.
Python programs, often called scripts, consist of definitions and instructions. A Python shell (a shell is a user interface that interprets and relays user input to an application and is part of the operating system (OS); the Python shell is an interactive command line interface) in a Python The interpreter evaluates the definition and executes the instructions. Usually, a new shell is created each time program execution is started. Usually, a window is associated with this shell.
File input/output functions are the most basic and indispensable functions when programming. Since file input/output functions are procedural instructions, each language has its own way of implementing them. Concrete implementations of file input/output in various languages are described below.
Among programming languages, the basic functionality is one element of the three functions of structured languages (1) sequential progression, (2) conditional branching, and (3) repetition, as described in the “History of Programming Languages” section. Here, we show implementations of repetition and branching in various languages.
Database technology refers to technology for efficiently managing, storing, retrieving, and processing data, and is intended to support data persistence and manipulation in information systems and applications, and to ensure data accuracy, consistency, availability, and security.
The following sections describe implementations in various languages for actually handling these databases.
A vector database is a type of database that primarily stores vector data and allows queries, searches, and other operations to be performed in vector space. vector database vendors have emerged. This has been particularly influenced by the rise of ChatGPT,
This is because vector databases can be used in configurations called RAGs to compensate for weaknesses in ChatGPT, such as handling the latest news and unpublished information, which ChatGPT is not very good at. Vector databases are designed to search for data based on vector similarity and to retrieve relevant data efficiently. Some also use algorithms such as k-NN (k nearest neighbor) to retrieve high-dimensional data and also use techniques such as quantization and partitioning to optimize retrieval performance.
This section describes examples of how servers described in “Server Technology” can be used in various programming languages. Server technology here refers to technology related to the design, construction, and operation of server systems that receive requests from clients over a network, execute requested processes, and return responses.
Server technologies are used in a variety of systems and services, such as web applications, API servers, database servers, and mail servers. Server technology implementation methods and best practices differ depending on the programming language and framework.
Raspberry Pi is a Single Board Computer (SBC), a small computer developed by the Raspberry Pi Foundation in the UK. Its name comes from a dessert called “Raspberry Pi,” which is popular in the UK.
This section provides an overview of the Raspberry Pi and describes various applications and concrete implementation examples.
Typically, IOT devices are small devices with sensors and actuators, and use wireless communication to collect sensor data and control actuators. Various communication protocols and technologies are used for wireless IoT control. This section describes examples of IoT implementations using this wireless technology in various languages.
In this article, I will discuss type hinting in Python, a dynamically typed language, using a type checker called mypy.
Comparison of asynchronous processing in several languages (pyhton javascript clojure, etc.)
Comparison of repetitive processing in various languages, which is one of the three functions of structured languages: (1) sequential progression, (2) conditional branching, and (3) repetition.
- Python textbook published by Kyoto University(1)(japanese)
- Python textbook published by Kyoto University(2)(japanese)
Web Application
Database technology refers to technology for efficiently managing, storing, retrieving, and processing data, and is intended to support data persistence and manipulation in information systems and applications, and to ensure data accuracy, consistency, availability, and security.
The following sections describe implementations in various languages for actually handling these databases.
This section describes examples of how servers described in “Server Technology” can be used in various programming languages. Server technology here refers to technology related to the design, construction, and operation of server systems that receive requests from clients over a network, execute requested processes, and return responses.
Server technologies are used in a variety of systems and services, such as web applications, API servers, database servers, and mail servers. Server technology implementation methods and best practices differ depending on the programming language and framework.
Web crawling is a technology to automatically collect information on the Web. This section describes an overview of web crawling, its applications, and concrete implementations using Python and Clojure.
A search system will be a system that searches databases and information sources based on a given query and returns relevant results, and will be capable of targeting various types of data, such as information, image, and voice search. The implementation of a search system involves elements such as database management, search algorithms, indexing, ranking models, and user interfaces, and a variety of technologies and algorithms are used, with the appropriate approach selected according to specific requirements and data types.
This section discusses specific implementation examples, focusing on Elasticsearch.
Multimodal search integrates multiple different information sources and data modalities (e.g., text, images, audio, etc.) to enable users to search for and retrieve information. This approach effectively combines information from multiple sources to provide more multifaceted and richer search results. This section provides an overview and implementation of this multimodal search, one using Elasticsearch and the other using machine learning techniques.
Elasticsearch is an open source distributed search engine for search, analysis, and data visualization that also integrates Machine Learning (ML) technology and can be leveraged for data-driven insights and predictions. It is a platform that can be used to achieve data-driven insights and predictions. This section describes various uses and specific implementations of machine learning technology in Elasticsearch.
Data encryption will be a technology to protect data from unauthorized access and information leakage by converting data in a non-reversible manner. Through encryption, data depends on a specific key and is converted into a form that cannot be understood by those who do not know the key, so that only those with the legitimate key can decrypt the data and restore it to its original state. This section describes various algorithms and implementation forms of this encryption technique.
Data compression is the process of reducing the size of data in order to represent information more efficiently. The main purpose of data compression is to make data smaller, thereby saving storage space and improving data transfer efficiency. This section describes various algorithms and their implementation in python for data compression.
Automata theory is a branch of the theory of computation and one of the most important theories in computer science. By studying abstract computer models such as finite state machines (FSMs), pushdown automata, and Turing machines, automata theory is applied to solve problems in formal languages, formal grammars, computability, computability, and natural language processing. This section provides an overview of this automata theory, its algorithms and various applications and implementations.
Dynamic Programming is a mathematical method for solving optimization problems, especially those with overlapping subproblems. Dynamic programming provides an efficient solution method because it dramatically reduces the amount of exponential computation by saving and reusing the results once computed. This section describes various algorithms and specific implementations in python for this dynamic programming.
WoT (Web of Things) will be a standardized architecture and protocol for interconnecting various devices on the Internet and enabling communication and interaction between devices. The WoT is intended to extend the Internet of Things (IoT), simplify interactions with devices, and increase interoperability.
This article describes general implementation procedures, libraries, platforms, and concrete examples of WoT implementations in python and C.
- Preprocessing for IoT
Pre-processing for processing Internet of Things (IoT) data is an important step in shaping the data collected from devices and sensors into a form that can be analyzed and used to feed machine learning models and applications. Below we discuss various methods related to IoT data preprocessing.
A distributed Internet of Things (IOT) system refers to a system in which different devices and sensors communicate with each other, share information, and work together. In this article, we will provide an overview and implementation examples of inter-device communication technology in this distributed IOT system.
Geographic Information Processing (GIP) refers to technologies and methods for acquiring, managing, analyzing, and displaying information about geographic locations and spatial data, and is widely used in the fields of Geographic Information Systems (GIS) and It is widely used in the field of Geographic Information Systems (GIS) and Location-based Systems (LBS). This section describes various applications of geographic information processing and concrete examples of implementation in python.
Displaying and animating graph snapshots on a timeline is an important technique for analyzing graph data, as it helps visualize changes over time and understand the dynamic characteristics of graph data. This section describes libraries and implementation examples used for these purposes.
This paper describes the creation of animations of graphs by combining NetworkX and Matplotlib, a technique for visually representing dynamic changes in networks in Python.
Methods for plotting high-dimensional data in low dimensions using dimensionality reduction techniques to facilitate visualization are useful for many data analysis tasks, such as data understanding, clustering, anomaly detection, and feature selection. This section describes the major dimensionality reduction techniques and their methods.
Gephi is an open-source graph visualization software that is particularly suitable for network analysis and visualization of complex data sets. Here we describe the basic steps and functionality for visualizing data using Gephi.
Mathematics
- Overview of Cross Entropy and Related Algorithms and Implementations
Cross Entropy is a concept commonly used in information theory and machine learning, especially in classification problems to quantify the difference between model predictions and actual data. Cross-entropy is derived from information theory, which uses the concept of “entropy” as a measure of the amount of information. Entropy is a measure of the uncertainty or difficulty of predicting information. It is greatest when the probability distribution is even and decreases as the probability concentrates on a particular value.
CP decomposition (CANDECOMP/PARAFAC) is a type of tensor decomposition and is one of the decomposition methods for multidimensional data. CP decomposition approximates a tensor as the sum of multiple rank-1 tensors. It is usually applied to tensors of three or more dimensions, and we will use a three-dimensional tensor as an example here.
Non-Negative Tensor Factorization (NTF) is a method for obtaining a representation of multidimensional data by decomposing a tensor (multidimensional array) into non-negative elements. and signal analysis, feature extraction, and dimensionality reduction.
Tucker decomposition is a method for decomposing multidimensional data and is a type of tensor decomposition; Tucker decomposition approximates a tensor as a product of several low-rank tensors.
Mode-based tensor decomposition is a method for decomposing a multidimensional data tensor into a product of lower-rank tensors, which are specifically used to decompose the tensor and extract latent structures and patterns in the data set. Tensor decomposition can also be viewed as a multidimensional extension of matrix decomposition (e.g., SVD).
PARAFAC2 (Parallel Factor 2) decomposition is one of the tensor decomposition methods, and is a type of mode-based tensor decomposition described in “Overview, Algorithm, and Implementation Examples of Mode-based Tensor Decomposition”. The usual PARAFAC (canonical decomposition) approximates tensors of three or more dimensions as a sum of lower-ranked tensors, but PARAFAC2 can be applied to tensors of more general geometry.
The Tensor Power Method is a type of iterative method for solving tensor singular value decomposition and eigenvalue problems, and is useful for finding approximate solutions to tensor singular values and eigenvalues. The following is a basic overview of the Tensor Power Method.
Alternating Least Squares (ALS) is a method for solving optimization problems using the Least Squares method, which is often used in the context of matrix and tensor decomposition. An overview of ALS is given below.
Alternating Least Squares for Tensor Factorization (ALS-TF) is a method for tensor factorization. ALS-TF is especially applied to recommendation systems and tensor data analysis.
Alternating Least Squares for Non-Negative Matrix Factorization (ALS-NMF) is a type of Non-Negative Matrix Factorization (NMF). NMF is a method for decomposing a matrix \(V \) with non-negativity constraints into a product of a non-negative matrix \(W \) and \(H \), and ALS-NMF optimizes it while keeping the non-negativity constraints.
Block Term Decomposition (BTD) is one of the methods for tensor data analysis. Tensor data is a multi-dimensional data structure similar to a two-dimensional matrix, and BTD aims to decompose the tensor data into low-rank block structures.
The random algorithm for tensor decomposition is a method for decomposing a large tensor into a product of smaller tensors, where the tensor is a multidimensional array and the tensor decomposition will aim to decompose that tensor into a product of multiple rank 1 tensors (or tensors of smaller rank). The random algorithm begins by approximating the tensor with a random matrix, and this approximation matrix is used as an initial estimate for finding a low-rank approximation of the tensor
Machine Learning / Natural Language Processing/Image Recognition
- Overview of Iterative Optimization Algorithms and Examples of Implementations
Iterative optimization algorithms are an approach that iteratively improves an approximate solution in order to find the optimal solution to a given problem. These algorithms are particularly useful in optimization problems and are used in a variety of fields. The following is an overview of iterative optimization algorithms.
- Overview of mini-batch learning and examples of algorithms and implementations
Mini-batch learning is one of the most widely used and efficient learning methods in machine learning, which is computationally more efficient and applicable to large data sets compared to the usual Gradient Descent method. This section provides an overview of mini-batch learning. Mini-batch learning is a learning method in which multiple samples (called mini-batches) are processed in batches, rather than the entire dataset at once, and the gradient of the loss function is calculated for each mini-batch and the parameters are updated using the gradient.
- Overview of interpolation methods and examples of algorithms and implementations
Interpolation is a method of estimating or complementing values between known data points, connecting points in a data set to generate a smooth curve or surface, which can then be used to estimate values at unknown points. Several major interpolation methods are discussed below.
Feature engineering refers to the extraction of useful information from a dataset and the creation of input features that machine learning models can use to make predictions and classification, and is an important process in the context of machine learning and data analysis. This section describes various methods and implementations of feature engineering.
- Model Quantization and Distillation
Model quantization (Quantization) and distillation (Knowledge Distillation) are methods for improving the efficiency of machine learning models and reducing resources during deployment.
- Overview of Model Distillation with Soft Target and Examples of Algorithms and Implementations
Model distillation by soft target (Soft Target) is a technique for transferring the knowledge of a large and computationally expensive teacher model to a small and efficient student model. Typically, soft target distillation focuses on teaching the probability distribution of the teacher model to the student model in a class classification task. Below we provide an overview of model distillation by soft targets.
- Model Lightening through Pruning and Quantization
Model lightening is an important technique for converting deep learning models to smaller, faster, and more energy efficient models. There are various approaches to model lightening, including pruning and quantization The following is a list of some of the most common approaches to model lightening.
- Overview of Post-training Quantization and Examples of Algorithms and Implementations
Post-training quantization is a method of quantizing a model after the training of a neural network has been completed, and this method converts the weights and activations of the model, which are usually expressed in floating-point numbers, into a form expressed in low-bit numbers such as integers. This reduces the model’s memory usage. This reduces model memory usage and improves inference speed. The following is an overview of post-training quantization.
- Overview of Model Distillation with FitNet and Examples of Algorithms and Implementations
FitNet is a model distillation method that allows small student models to learn knowledge from large teacher models. Below we provide an overview of model distillation with FitNet.
- Overview of Quantization-Aware Training and Examples of Algorithms and Implementations
Quantization-Aware Training (QAT) is one of the training methods for effectively quantizing (quantizing) neural networks. Quantization is the process of expressing the weights and activations of a model in low-bit numbers, such as integers, instead of floating-point numbers. Quantization-Aware Training is one of the methods to incorporate this quantization into the model during training to obtain a model that takes into account the effects of quantization during training.
- Attention Transfer Model Distillation Overview, Algorithm, and Implementation Examples
Attention Transfer is one of the methods for model distillation in deep learning. Model distillation is a method for transferring knowledge from a large and computationally demanding model (teacher model) to a small and lightweight model (student model). This allows student models to perform as well as teacher models while reducing the use of computational resources and memory.
- Measures for Dealing with Unknown Models in Machine Learning
Measures for machine learning models to deal with unknown data have two aspects: ways to improve the generalization performance of the model and ways to design how the model should deal with unknown data.
- Overview of Hard Negative Mining and Examples of Algorithms and Implementations
Hard Negative Mining is a method of focusing on difficult negative samples (negative examples) in the field of machine learning, especially in tasks such as anomaly detection and object detection. This allows the model to deal with more difficult cases and is expected to improve performance.
- NLP Processing of Long Sentences by Sentence Segmentation
Sentence segmentation is an important step in the NLP (natural language processing) processing of long sentences. By segmenting long sentences into sentences, the text can be easily understood and analyzed, making it applicable to a variety of tasks. Below is an overview of sentence segmentation in NLP processing of long sentences.
- How to Deal with Overlearning in Machine Learning
Overfitting is a phenomenon in which a machine learning model overfits the training data, resulting in poor generalization performance for new data.
Word Sense Disambiguation (WSD) is one of the key challenges in the field of Natural Language Processing (NLP). The goal of this technique is to accurately identify the meaning of a word in a sentence when it is used in multiple senses. In other words, when the same word has different meanings in different contexts, WSD tries to identify the correct meaning of the word, which is an important preprocessing step in various NLP tasks such as machine translation, information retrieval, and question answering systems. If the system can understand exactly which meaning is being used for a word in a sentence, it is more likely to produce more relevant and meaningful results.
Methods for extracting emotion from textual data include, specifically, dividing sentences into tokens, using machine learning algorithms to understand word meaning and context, and training models using a dataset for emotion analysis to predict the emotion context for unknown text
Sentiment Lexicons (Sentiment Polarity Lexicons) are used to indicate how positive or negative a word or phrase is. There are several statistical methods to analyze sentiment using this dictionary, including (1) simple count-based methods, (2) weighted methods, (3) combined TF-IDF methods, and (4) machine learning approaches.
Self-Supervised Learning (SLS) is a field of machine learning, an approach to learning from unlabeled data, and the SLS approach is a widely used method for training language models and learning expressions. The following is an overview of the Self-Supervised Learning approach to language processing.
A Hesse matrix is a matrix representation of the second-order partial derivatives of a multivariate function, in which the second-order partial derivatives for each variable of the multivariate function are stored in the Hesse matrix, just as the second-order derivatives of a single variable function are considered as second-order derivatives. Hesse matrices play an important role in many mathematical and scientific applications, such as nonlinear optimization and numerical analysis.
Cross-Entropy Loss is one of the common loss functions used in machine learning and deep learning to evaluate and optimize the performance of models in classification tasks, especially in binary classification (selecting one of two classes) and multi-class classification (selecting one of three or more It is a widely used method for binary classification (selecting one of two classes) and multiclass classification (selecting one of three or more classes) problems, among others.
The Gelman-Rubin statistic (or Gelman-Rubin diagnostic, Gelman-Rubin statistical test) is a statistical method for diagnosing convergence of Markov chain Monte Carlo (MCMC) sampling methods, particularly when MCMC sampling is done with multiple chains, where each chain will be used to evaluate whether they are sampled from the same distribution. This technique is often used in the context of Bayesian statistics. Specifically, the Gelman-Rubin statistic evaluates the ratio between the variability of the sample from multiple MCMC chains and the variability within each chain, and this ratio will be close to 1 if statistical convergence is achieved.
- Overview of Kronecker-factored Approximate Curvature (K-FAC) matrix and related algorithms and implementation examples
Kronecker-factored Approximate Curvature (K-FAC) is a method for efficiently approximating the inverse of the Hessian matrix in machine learning optimization problems, as described in “Hesse Matrix and Regularity”. This method has attracted attention as an efficient and scalable optimization method, especially in the training of neural networks. K-FAC was developed to efficiently approximate the Fisher information matrix or the inverse of the Hesse matrix in neural network optimization problems, as described in “Overview of the Fisher Information Matrix and Related Algorithms and Examples of Implementations. This makes it possible to train neural networks with high efficiency even at large scales.
- Overview of the Fisher Information Matrix and Related Algorithms and Examples of Implementations
The Fisher information matrix is a concept used in statistics and information theory to provide information about probability distributions. This matrix is used to provide information about the parameters of a statistical model and to evaluate its accuracy. Specifically, it contains information about the expected value of the derivative of the probability density function (or probability mass function) with respect to its parameters.
- Overview of Classification Problems Using Fisher’s Computational Method and Examples of Algorithms and Implementations
Fisher’s Linear Discriminant is a method for constructing a linear discriminant model to distinguish between two classes, which aims to find a projection that maximizes the variance between classes and minimizes the variance within classes. Specifically, the following steps are used to construct the model.
- Block K-FAC Overview, Algorithm, and Implementation Examples
Block K-FAC (Block Kronecker-factored Approximate Curvature) is a kind of curve chart (curvature information) approximation method used in deep learning model optimization.
The Cramér-Rao lower bound provides a lower bound in statistics to measure how much uncertainty an estimator has. Information Matrix” described in “Overview of the Fisher Information Matrix and Related Algorithms and Examples of Implementations. The procedure for deriving the CRLB is described below.
- Overview of Monte Carlo Dropout and Examples of Algorithms and Implementations
Monte Carlo Dropout is a method for estimating uncertainty in neural network inference using dropout. Usually, dropout is a method to promote network generalization by randomly disabling nodes during training, but Monte Carlo Dropout uses this method during inference.
- Overview of Procrustes Analysis and Related Algorithms and Examples of Implementations
Procrustes analysis is a method for finding the optimal rotation, scaling, and translation between corresponding point clouds of two datasets. This method is mainly used when two datasets represent the same object or shape, but need to be aligned by rotation, scaling, or translation.
- About Sequential Quadratic Programming
Sequential Quadratic Programming (SQP) is an iterative optimization algorithm for solving nonlinear optimization problems with nonlinear constraints. The SQP method is widely used as a numerical solution method for constrained optimization problems, especially in engineering, economics, transportation planning, machine learning, control system design, and many other areas of application.
Newton’s method (Newton’s method) is one of the iterative optimization algorithms for finding numerical solutions to nonlinear equations and functions, and is mainly used to find roots of equations, making it a suitable method for finding minima and maxima of continuous functions as well. Newton’s method is used in many machine learning algorithms because of its fast convergence.
- Modified Newton Method
The Modified Newton Method is an algorithm developed to improve the regular Newton-Raphson method to address several issues, and the main objective of the Modified Newton Method will be to improve convergence and numerical stability.
- Quasi-Newton Method
The Quasi-Newton Method (QNM) is an iterative method for solving nonlinear optimization problems. This algorithm is a generalization of the Newton method, which searches for the minimum of the objective function without computing higher derivatives (Hesse matrix). The quasi-Newton method is relatively easy to implement because it uses an approximation of the Hesse matrix and does not require an exact calculation of the Hesse matrix.
- Newton-Raphson Method
The Newton-Raphson Method (Newton-Raphson Method) is an iterative method for numerical solution of nonlinear equations and for finding the roots of a function, and the algorithm is used to approximate the zero point of a continuous function, starting from an initial estimated solution. The Newton-Raphson method converges quickly when the function is sufficiently smooth and is particularly effective when first derivatives (gradients) and second derivatives (Hesse matrices) can be computed.
- The vanishing gradient problem and its countermeasures
The vanishing gradient problem is one of the problems that occur mainly in deep neural networks and often occurs when the network is very deep or when a specific architecture is used.
- Overview of the Hilbert Wand Transform and Examples of Algorithms and Implementations
The Hilbert transform (Hilbert transform) is an operation widely used in signal processing and mathematics, and it can be a technique used to introduce an analyticity (analytic property) of a signal. The Hilbert transform converts a real-valued signal into a complex-valued signal, and the complex-valued signal obtained by the Hilbert transform can be used to extract phase and amplitude information from the original real-valued signal.
- About Residual Coupling
Residual Connection is a method for directly transferring information across layers in deep learning networks, which was introduced to address the problem of gradient loss and gradient explosion, especially when training deep networks. Residual coupling was proposed by Kaiming He et al. at Microsoft Research in 2015 and has since been very successful.
- Overview of the Davidon-Fletcher-Powell (DFP) method, its algorithm, and examples of its implementation
The DFP method (Davidon-Fletcher-Powell method) is one of the numerical optimization methods and is particularly suitable for nonlinear optimization problems. This method is characterized by using a quadratic approximation approach to find the optimal search direction, and the DFP method belongs to the category of quasi-Newton methods, which seek the optimal solution while updating the approximation of the inverse of the Hesse matrix.
Search Algorithm (Search Algorithm) refers to a family of computational methods used to find a target within a problem space. These algorithms have a wide range of applications in a variety of domains, including information retrieval, combinatorial optimization, game play, route planning, and more. This section describes various algorithms, their applications, and specific implementations with respect to these search algorithms.
- Heuristic Search (Hill Climbing, Greedy Search, etc.) Based Structural Learning
Structural learning based on heuristic search is a method that combines heuristic methods for searching the architecture and hyperparameters of machine learning models to find the optimal model or structure, and heuristics are intuitive and simple rules or approach. Below we describe some common methods related to heuristic search-based structure learning.
- Overview of the Calton Method (Cultural Algorithm) and Examples of Application and Implementation
Cultural Algorithm is a type of evolutionary algorithm that extends evolutionary algorithms by introducing cultural elements. programming are representative examples. The Calton method introduces a cultural component to these evolutionary algorithms, and becomes one that takes into account not only the evolution of individuals, but also the transfer of knowledge and information between individuals.
- Counting Problem Overview, Algorithm and Implementation Examples
Counting problems (counting problems) are one of the most frequently tackled problems in mathematics, such as combinatorics and probability theory, which are tasks often associated with finding the number of combinations or permutations as a problem of counting the total number of objects satisfying certain conditions. These problems are solved using mathematical principles and formulas, and concepts such as permutations, combinations, and binomial coefficients are often used, and depending on the problem, the respective formula must be chosen according to the nature of the problem.
- Overview of Optimization by Integer Linear Programming (ILP) and Examples of Algorithms and Implementations
Integer Linear Programming (ILP) is a method for solving mathematical optimization problems, especially for finding integer solutions under constraints. ILP is a type of Linear Programming (LP) with the additional conditions that the objective function and constraints are linear and the variables take integer values.
Exponential Smoothing is a statistical method used for forecasting and smoothing time series data, especially for forecasting future values based on past observations. Exponential smoothing is a simple but effective method that allows for weighting against time and adjusting for the effect of past data.
Self-Adaptive Search Algorithms, or Self-Adaptive Search Algorithms, are a family of algorithms used in the context of evolutionary computation and optimization, where the parameters and strategies within the algorithm are characterized by adaptive adjustment to the problem. These algorithms are designed to adapt to changes in the nature of the problem and the environment in order to efficiently find the optimal solution. This section describes various algorithms and examples of implementations with respect to this self-adaptive search algorithm.
Multi-Objective Search Algorithm (Multi-Objective Optimization Algorithm) is an algorithm for optimizing multiple objective functions simultaneously. Multi-objective optimization aims to find a balanced solution (Pareto optimal solution set) among multiple optimal solutions rather than a single optimal solution, and such problems have been applied to many complex systems and decision-making problems in the real world. This section provides an overview of this multi-objective search algorithm and examples of algorithms and implementations.
- Overview of the Minimax Method and Examples of Algorithms and Implementations
The minimax method is a type of search algorithm widely used in game theory and artificial intelligence, which is used to select the optimal move in a perfect information game (a game in which both players know all information). Typical games include chess, shogi, Othello, and Go.
- Alpha-beta pruning: overview, algorithm, and implementation examples
Alpha-beta pruning is a type of search algorithm used in the fields of artificial intelligence and computer games. This is a common approach used in combination with tree search algorithms such as the minimax method described in “Overview of the Minimax Method, Algorithms and Examples of Implementation. This algorithm is used to efficiently find a solution by reducing unnecessary search when searching the tree structure of a game. Specifically, the possible combinations of moves in a game are represented by a tree structure, and unnecessary moves are removed during the search, thereby reducing computation time.
- Overview of Monte Carlo Tree Search and Examples of Algorithms and Implementations
Monte Carlo Tree Search (MCTS), a type of decision tree search, is a probabilistic method for exploring the state space of a game to find the optimal action, and is a particularly effective approach in games and decision-making problems.
- Overview of UCT (Upper Confidence Bounds for Trees), Algorithm and Example Implementation
UCT (Upper Confidence Bounds for Trees) is an algorithm used in the selection phase of Monte Carlo Tree Search (MCTS), which aims to balance the search value of each node in the search. It is important to strike a balance between exploration and utilization. That is, the more nodes being explored are visited, the higher the value of the node will be estimated, but at the same time, the unexplored nodes will be given an appropriate opportunity to be explored.
- Overview of Information Set Monte Carlo Tree Search (ISMCTS) and Examples of Algorithms and Implementations
Information Set Monte Carlo Tree Search (ISMCTS) is a variant of Monte Carlo Tree Search (MCTS) used in games such as incomplete information games (e.g. poker) and information hiding games (e.g. Go, Shogi). The characteristic feature of this method is that it handles groups of game states, called information sets, when searching the game tree by applying MCTS.
- Overview of Nested Monte Carlo Search (NMC) and Examples of Algorithms and Implementations
Nested Monte Carlo Search (NMC) is a type of Monte Carlo Tree Search (MCTS), which is a method for efficiently exploring a search space.
- Rapid Action Value Estimation (RAVE) Overview, Algorithm, and Example Implementation
Rapid Action Value Estimation (RAVE) is a game tree search method developed as an extension of Monte Carlo Tree Search (MCTS) described in “Overview of Monte Carlo Tree Search, Algorithms and Examples”. RAVE is used to estimate the value of moves selected during game tree search. While the usual MCTS uses statistics of the moves explored to estimate the value of moves when the model is incomplete or as the search progresses, RAVE improves on this and aims to find suitable moves more quickly.
A ranking algorithm is a method for sorting a given set of items in order of most relevance to the user, and is widely used in various fields such as search engines, online shopping, and recommendation systems. This section provides an overview of common ranking algorithms.
- Random Forest Ranking Overview, Algorithm and Implementation Examples
Random Forest is a very popular ensemble learning method in the field of machine learning (a method that combines multiple machine learning models to obtain better performance than individual models). This approach combines multiple Decision Trees to build a more powerful model. There are many variations in ranking features using random forests.
- Diversity-Promoting Ranking Overview, Algorithm, and Implementation Example
Diversity-Promoting Ranking is one of the methods that play an important role in information retrieval and recommendation systems, which aim to make users’ information retrieval results and the list of recommended items more diverse and balanced. This will be the case. Usually, the purpose of ranking is to display items that match the user’s interests at the top, but at this time, multiple items with similar content and characteristics may appear at the top. For example, in a product recommendation system, similar items or items in the same category often appear at the top of the list. However, because these items are similar, they may not adequately cover the user’s interests, leading to information bias and limiting choices, and diversity promotion ranking is used to address these issues.
Exploratory Ranking is a technique for identifying items that are likely to be of interest to users in ranking tasks such as information retrieval and recommendation systems. This technique aims to find the items of most interest to the user among ranked items based on the feedback given by the user.
- Overview of Maximum Marginal Relevance (MMR) and Examples of Algorithms and Implementations
Maximum Marginal Relevance (MMR) is a ranking method for information retrieval and information filtering that aims to optimize the ranking of documents provided to users by information retrieval systems. MMR was developed as a method for selecting documents that are relevant to the user’s interests from among multiple documents. The method will rank documents based on both the relevance and diversity of each document, specifically emphasizing the selection of documents that are highly relevant but have low similarity to other options.
- Overview of Rank SVM, Algorithm and Implementation Example
Rank SVM (Ranking Support Vector Machine) is a type of machine learning algorithm applied to ranking tasks, especially for ranking problems in information retrieval and recommendation systems. Related papers include “Optimizing Search Engines using Clickthrough Data” and “Ranking Support Vector Machine with Kernel Approximation”.
- Diversified Top-k Retrieval (DTkR) Overview, Algorithm and Example Implementation
Diversified Top-k Retrieval (DTkR) is a method for obtaining diversified top-k search results in information retrieval and ranking tasks, aiming to obtain search results with different perspectives and diversity rather than simple Top-k results. In general Top-k search, the objective is simply to obtain the top k results with the highest scores, but similar results tend to rank high and lack diversity. On the other hand, DTkR aims to make the search results more diverse and different, and can perform information retrieval with diversity that cannot be obtained with simple Top-k search results.
- Overview of Submodular Diversification and examples of algorithms and implementations
Submodular Diversification is a method for selecting the top k items with diversity in information retrieval and ranking tasks. The basis of Submodular Diversification is the Submodular function, also described in “Submodular Optimisation and Machine Learning”, which is a set function \( f: 2^V \rightarrow \mathbb{R} \), with the following properties.
- Overview of Cluster-based Diversification and examples of algorithms and implementations
Cluster-based Diversification is a method for introducing diversity into a recommendation system using clustering of items. In this method, similar items are grouped into the same cluster and diversity is achieved by selecting items from different clusters.
- Overview, algorithms and implementation examples of neural ranking models
A neural ranking model is a type of machine learning model used in search engines and recommendation systems, where the main objective is to sort items (e.g. web pages, products, etc.) in the best ranking based on given queries and user information. For a typical search engine, it is important to display first the web pages that are most relevant to the user’s query, and to achieve this, the search engine considers a number of factors to determine the ranking of web pages. These include keyword match, page credibility and the user’s previous click history.
- Overview, algorithms and implementation examples of personalised ranking
Personalised ranking is a ranking method that provides items in the most suitable rank for each user. While general ranking systems present items in the same rank for all users, personalised ranking takes into account the individual preferences and behaviour of the user and Personalised ranking takes into account the user’s individual preferences and behaviour and ranks items in the most appropriate order for that user. The purpose of personalised ranking is to increase user engagement by showing items that are likely to be of interest to the user at a higher rank, increase user engagement, increase user purchases, clicks and other actions, and increase conversion rates Increased conversion rates, users find the information and products they are looking for more quickly, which increases user satisfaction, which increases user satisfaction, and so on.
- Overview of Beam Search and Examples of Algorithms and Implementations
Beam Search is a search algorithm mainly applied to combinatorial optimization and finding meaningful solutions. It is mainly used in areas such as machine translation, speech recognition, and natural language processing.
Automatic machine learning (AutoML) refers to methods and tools for automating the process of designing, training, and optimizing machine learning models.AutoML is particularly useful for users with limited machine learning expertise or those seeking to develop efficient models, with the following main goals. This section provides an overview of this AutoML and examples of various implementations.
Byte Pair Encoding (BPE) is a text encoding method used to compress and tokenize text data. BPE is widely used in Natural Language Processing (NLP) tasks in particular and is known as an effective tokenization method.
SentencePiece is an open source library and toolkit for tokenizing text data. NLP) tasks.
InferSent is a method for learning semantic representations of sentences in natural language processing (NLP) tasks. The following is a summary of the main features of InferSent.
Skip-thought vectors, neural network models that generate semantic representations of sentences and are designed to learn context-aware sentence embedding (embedding), were proposed in 2015 by Kiros et al. proposed by Kiros et al. in 2015. The model aims to embed a sentence into a continuous vector space, taking into account the context before and after the sentence. The main concepts and structure of Skip-thought vectors are described below.
The Unigram Language Model Tokenizer (UnigramLM Tokenizer) is a tokenization algorithm used in natural language processing (NLP) tasks. Unlike conventional algorithms that tokenize words, the Unigram Language Model Tokenizer focuses on tokenizing partial words (subwords).
- Overview, Algorithm, and Example Implementation of WordPiece
WordPiece is one of the tokenization algorithms used in natural language processing (NLP) tasks, especially in models such as BERT (Bidirectional Encoder Representations from Transformers), which is described in “Overview of BERT and Examples of Algorithms and Implementations. BERT (Bidirectional Encoder Representations from Transformers),” which is also described in “BERT Overview, Algorithms, and Example Implementations.
GloVe (Global Vectors for Word Representation) is a type of algorithm for learning word embeddings. GloVe is specifically designed to capture the meaning of words and has excellent ability to capture the semantic relevance of words. This section provides an overview, algorithm, and example implementation with respect to Glove.
FastText is an open source library for natural language processing (NLP) developed by Facebook that can be used to learn word embeddings and perform NLP tasks such as text classification. Here we describe the FastText algorithm and an example implementation.
- Skipgram Overview, Algorithm and Example Implementation
Skip-gram is a method for learning distributed representations of words (word embedding), which is widely used in the field of natural language processing (NLP) to quantify similarity and relevance of meanings by capturing word meanings as vector representations. It is also used in GNNs such as DeepWalk, which is described in “Overview of DeepWalk, Algorithms, and Examples of Implementations”.
ELMo (Embeddings from Language Models) is one of the methods of word embeddings (Word Embeddings) used in the field of natural language processing (NLP), which was proposed in 2018 and has been very successful in subsequent NLP tasks. In this section, we provide an overview of this ELMo, its algorithm and examples of its implementation.
BERT (Bidirectional Encoder Representations from Transformers), BERT was presented by Google researchers in 2018 and is a deep neural network model pre-trained with a large text corpus and is one of the very successful pre-training models in the field of natural language processing (NLP). This section provides an overview of this BERT, its algorithms and examples of implementations.
- Overview of GPT and Examples of Algorithms and Implementations
GPT (Generative Pre-trained Transformer) is a pre-trained model for natural language processing developed by Open AI, based on the Transformer architecture and trained by unsupervised learning using large data sets. .
ULMFiT (Universal Language Model Fine-tuning) was proposed by Jeremy Howard and Sebastian Ruder in 2018 to effectively fine-tune pre-trained language models in natural language processing (NLP) tasks. It is an approach for fine tuning. The approach aims to achieve high performance on a variety of NLP tasks by combining transfer learning with fine tuning at each stage of training.
Transformer was proposed by Vaswani et al. in 2017 and will be one of the neural network architectures that have led to revolutionary advances in the field of machine learning and natural language processing (NLP). This section provides an overview of this Transformer model and its algorithm and implementation.
- About Transformer XL
Transformer XL will be one of the extended versions of Transformer, a deep learning model that has proven successful in tasks such as natural language processing (NLP). Transformer XL is designed to more effectively model long-term dependencies in context and is able to process longer text sequences than previous Transformer models.
- Overview of the Transformer-based Causal Language Model with Algorithms and Example Implementations
The Transformer-based Causal Language Model is a type of model that has been very successful in Natural Language Processing (NLP) tasks. The Transformer model (Transformer-based Causal Language Model) is a very successful model for natural language processing (NLP) tasks and is based on the Transformer architecture described in “Overview of the Transformer Model and Examples of Algorithms and Implementations. The following is an overview of the Transformer-based Causal Language Model.
- About Relative Positional Encoding
Relative Positional Encoding (RPE) is a method for neural network models that use the transformer architecture to incorporate relative positional information of words and tokens into the model. Although transformers have been very successful in many tasks such as natural language processing and image recognition, they are not good at directly modeling the relative positional relationships between tokens. Therefore, RPE is used to provide relative location information to the model.
- Overview of GANs and their various applications and implementations
GAN (Generative Adversarial Network) is a machine learning architecture that is called a generative adversarial network. This model was proposed by Ian Goodfellow in 2014 and has since been used with great success in many applications. This section provides an overview of this GAN, its algorithms and various application implementations.
Federated Learning is a new approach to training machine learning models that addresses the challenges of privacy protection and efficient model training in distributed data environments. Unlike traditional centralized model training, Federated Learning trains models on the device or client itself and performs distributed learning without sending models to a central server. This section provides an overview of Federated Learning, its various algorithms, and examples of implementations.
Parallel distributed processing in machine learning is a process that distributes data and calculations across multiple processing units (CPUs, GPUs, computer clusters, etc.) and simultaneously processes them to reduce processing time and improve scalability, and plays an important role when processing large data sets and complex models. It plays an important role in processing large data sets and complex models. This section describes concrete implementation examples of parallel distributed processing in machine learning in on-premise/cloud environments.
The gradient method is one of the widely used methods in machine learning and optimization algorithms, whose main goal is to iteratively update parameters in order to find the minimum (or maximum) value of a function. In machine learning, the goal is usually to minimize the cost function (also called loss function). For example, in regression and classification problems, a cost function is defined to represent the error between predicted and actual values, and it helps to find the parameter values that minimize this cost function.
This section describes various algorithms for this gradient method and examples of implementations in various languages.
- Stochastic Gradient Descent (SGD) Overview, Algorithm and Implementation Examples
Stochastic Gradient Descent (SGD) is an optimization algorithm widely used in machine learning and deep learning. parameters are updated. The basic concepts and features of SGD are described below.
- Overview of Natural Gradient Descent and Examples of Algorithms and Implementations
Natural Gradient Descent is a type of Stochastic Gradient Descent (SGD) described in “Overview of Stochastic Gradient Descent (SGD), Algorithms, and Implementation Examples. It is a type of Stochastic Gradient Descent (SGD), which is an optimization method for efficiently updating model parameters, and is an approach that takes into account the geometric structure of the model parameter space and appropriately scales the gradient information.
Gaussian-Hermite Integration is a method of numerical integration often used for stochastic problems, especially those in which the probability density function has a Gaussian distribution (normal distribution), and for integrals such as the wave function in quantum mechanics. The Gauss-Hermite polynomial is used to approximate the integral. This section provides an overview, algorithm, and implementation of the Gauss-Hermite integral.
- Overview of the Ornstein-Uhlenbeck process and examples of algorithms and implementations
The Ornstein-Uhlenbeck process is a type of stochastic process, especially one used to model the motion of a random variable in continuous time. The process has been widely applied in various fields, including physics, finance, statistics, and machine learning. The Ornstein-Uhlenbeck process is obtained by introducing resilience into Brownian motion (or Wiener process). Normally, Brownian motion represents random fluctuations, but in the Ornstein-Uhlenbeck process, a recovery force is added to that random fluctuation to move it back toward some average.
Model Predictive Control (MPC) is a control theory technique that uses a model of the control target to predict future states and outputs, and an online optimization method to calculate optimal control inputs. MPC is used in a variety of industrial and control applications.
- Broyden-Fletcher-Goldfarb-Shanno (BFGS) Method
The Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is a type of numerical optimization algorithm for solving nonlinear optimization problems. The BFGS method is known as the quasi-Newton method and provides effective solutions to many real-world optimization problems.
- Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) Method
The Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method is an algorithm that uses the “Broyden-Fletcher -The L-BFGS method, like the BFGS method, is a form of the quasi-Newton method that uses an inverse approximation of the Hesse matrix The L-BFGS method, like the BFGS method, is a form of quasi-Newtonian method that minimizes the objective function using an inverse approximation of the Hesse matrix. However, the L-BFGS method is designed to reduce memory consumption and is particularly suited to high-dimensional problems.
- Conjugate Gradient Method
The conjugate gradient method is a numerical algorithm used for solving linear systems of linear equations and nonlinear optimization problems. It can also be applied as a quasi-Newtonian method for nonlinear optimization problems.
- Trust Region Method
The Trust Region Method is an optimization algorithm for solving nonlinear optimization problems, which is used to find a solution under constraints in minimizing (or maximizing) an objective function. The Trust Region Method is suitable for constrained optimization problems and nonlinear least squares problems, and is particularly useful for finding globally optimal solutions.
In machine learning tasks, recall is an indicator mainly used for classification tasks. To achieve 100% recall means, in the case of a general task, to extract all the data (positives) that should be found without omission, and this is something that frequently appears in tasks involving real-world risks.
However, achieving 100% reproducibility is generally difficult to achieve, as it is limited by the characteristics of the data and the complexity of the problem. In addition, the pursuit of 100% reproducibility may lead to an increase in the percentage of false positives (i.e., mistaking an originally negative result for a positive result), so it is necessary to consider the balance between these two factors.
This section describes the issues that must be considered in order to achieve a 100% reproducibility rate, as well as approaches and specific implementations to address these issues.
Fermi estimation (Fermi estimation) is a method for making rough estimates when precise calculations or detailed data are not available and is named after the physicist Enrico Fermi. Fermi estimation is widely used as a means to quickly find approximate answers to complex problems using logical thinking and appropriate assumptions. In this article, we will discuss how this Fermi estimation can be examined using artificial intelligence techniques.
This section provides an overview of machine learning/data analysis using pyhton and an introduction to typical libraries.
Statistical Hypothesis Testing is a method in statistics that probabilistically evaluates whether a hypothesis is true or not, and is used not only to evaluate statistical methods, but also to evaluate the reliability of predictions and to select and evaluate models in machine learning. It is also used in the evaluation of feature selection as described in “Explainable Machine Learning,” and in the verification of the discrimination performance between normal and abnormal as described in “Anomaly Detection and Change Detection Technology,” and is a fundamental technology. This section describes various statistical hypothesis testing methods and their specific implementations.
Kullback-Leibler Variational Estimation is a method for estimating approximate probabilistic models of data by evaluating and minimizing differences between probability distributions. It is widely used in the context of Its main applications are as follows.
- Overview of the Dirichlet distribution and related algorithms and implementation examples
The Dirichlet distribution is a type of multivariate probability distribution that is mainly used for modeling the probability distribution of random variables. The Dirichlet distribution is a probability distribution that generates a vector (multidimensional vector) consisting of K non-negative real numbers.
A softmax function is a function used to convert a vector of real numbers into a probability distribution, which is usually used to interpret the output of a model as probabilities in machine learning classification problems. The softmax function calculates the exponential function of the input elements, which can then be normalized to obtain a probability distribution.
k-means is one of the algorithms used in the machine learning task called clustering, a method that can be used in a variety of tasks. Clustering here refers to the method of dividing data points into groups (clusters) with similar characteristics. The k-means algorithm aims to divide the given data into a specified number of clusters. This section describes the various algorithms of this k-means and their specific implementations.
Decision Tree is a tree-structured classification and regression method used as a predictive model for machine learning and data mining. Since decision trees can construct conditional branching rules in the form of a tree to predict classes (classification) and values (regression) based on data characteristics (features), they can white box machine learning results, as described in “Explainable Machine Learning”. This section describes various algorithms for decision trees and concrete examples of their implementation.
The issue of small amount of data to be trained (small data) is a problem that appears in various tasks as a factor that reduces the accuracy of machine learning. Machine learning with small data can be approached in various ways, taking into account data limitations and the risk of overlearning. This section discusses the details of each approach and implementation examples.
- Overview of SMOTE (Synthetic Minority Over-sampling Technique), Algorithm and Implementation Examples
SMOTE (Synthetic Minority Over-sampling Technique) is a technique for complementing under-sampling by combining minority class samples in datasets with unbalanced class distributions. used to improve model performance, primarily in machine learning class classification tasks. An overview of SMOTE is given below.
Ensemble Learning is a type of machine learning that combines multiple machine learning models to build a more powerful predictive model. Combining multiple models rather than a single model can improve the prediction accuracy of the model. Ensemble learning has been used successfully in a variety of applications and is one of the most common techniques in machine learning.
- Overview of Transfer Learning, Algorithms, and Examples of Implementations
Transfer learning, a type of machine learning, is a technique for applying a model or knowledge learned in one task to a different task. Transfer learning is usually useful when a new task requires little data or high performance. This section provides an overview of transfer learning and various algorithms and implementation examples.
- Overview of genetic algorithms, application examples, and implementation examples
Genetic algorithm (GA) is a type of evolutionary computation, and is an optimization algorithm for optimizing problems by imitating the evolutionary process in nature, and is used for optimization, exploration, machine learning, and machine design. This is a method that has been applied to a variety of problems. The basic elements and mechanism of the genetic algorithm are described below.
- Overview of Genetic Programming (GP) and its algorithms and implementations
Genetic Programming (GP) is a type of evolutionary algorithm that is widely used in machine learning and optimization. An overview of GP is given below.
- Overview of Gene Expression Programming (GEP) and Examples of Algorithms and Implementations
Gene Expression Programming (GEP) is a type of evolutionary algorithm, a method that is particularly suited for the evolutionary generation of mathematical expressions and programs. This technique is used to evolve the form of a mathematical expression or program to help find the best solution for a particular task or problem. The main features and overview of GEP are described below.
Meta-Learners are one of the key concepts in the domain of machine learning and can be understood as “algorithms that learn learning algorithms. In other words, Meta-Learners can be described as an approach to automatically acquire learning algorithms that can be adapted to different tasks and domains. This section describes this Meta-Learners concept, various algorithms and concrete implementations.
Self-Supervised Learning is a type of machine learning and can be considered as a type of supervised learning. While supervised learning uses labeled data to train models, self-supervised learning uses the data itself instead of labels to train models. This section describes various algorithms, applications, and implementations of self-supervised learning.
- Active Learning Techniques in Machine Learning
Active learning in machine learning (Active Learning) is a strategic approach to effectively selecting labeled data to improve model performance. Typically, training machine learning models requires large amounts of labeled data, but since labeling is costly and time consuming, active learning increases the efficiency of data collection.
- Target Domain-Specific Fine Tuning in Machine Learning Technology
Target domain-specific fine tuning refers to the process in machine learning techniques of adjusting a model from a general, pre-trained model to one that is more suitable for a specific task or tasks related to a domain. It is a form of transition learning and is performed in the following steps.
- Overview of Question-Answering Learning and Examples of Algorithms and Implementations
Question Answering (QA) is a branch of natural language processing in which the task is to generate appropriate answers to given questions. retrieval, knowledge-based query processing, customer support, work efficiency, and many other applications. This paper provides an overview of question-answering learning, its algorithms, and various implementations.
- Overview of DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and Examples of Applications and Implementations
DBSCAN is a popular clustering algorithm in data mining and machine learning that aims to discover clusters based on the spatial density of data points rather than assuming the shape of the clusters. This section provides an overview of this DBSCAN, its algorithm, various application examples, and a concrete implementation in python.
FP-Growth (Frequent Pattern-Growth) is an efficient algorithm for data mining and frequent pattern mining, and is a method used to extract frequent patterns (itemsets) from transaction data sets. In this paper, we describe various applications of the FP-Growth algorithm and an example implementation in python.
Maximum Likelihood Estimation (MLE) is an estimation method used in statistics. This method is used to estimate the parameters of a model based on given data or observations. Maximum likelihood estimation attempts to maximize the probability that data will be observed when the values of the parameters are changed. This section provides an overview of this maximum likelihood estimation method, its algorithm, and an example implementation in python.
The EM algorithm (Expectation-Maximization Algorithm) is an iterative optimization algorithm widely used in statistical estimation and machine learning. In particular, it is often used for parameter estimation of stochastic models with latent variables.
Here, we provide an overview of the EM algorithm, the flow of applying the EM algorithm to mixed models, HMMs, missing value estimation, and rating prediction, respectively, and an example implementation in python.
The EM (Expectation Maximization) algorithm can also be used as a method for solving the Constraint Satisfaction Problem. This approach is particularly useful when there is incomplete information, such as missing or incomplete data. This paper describes various applications of the constraint satisfaction problem using the EM algorithm and its implementation in python.
- Stochastic Gradient Langevin Dynamics (SGLD) Overview, Algorithm and Implementation Examples
Stochastic Gradient Langevin Dynamics (SGLD) is a stochastic optimization algorithm that combines stochastic gradient and Monte Carlo methods. SGLD is widely used in Bayesian machine learning and Bayesian statistical modeling to estimate the posterior distribution.
- Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) Overview, Algorithm, and Implementation Examples
Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) is a type of Hamiltonian Monte Carlo (HMC), which is a stochastic sampling method combined with a stochastic gradient method and is used to estimate the posterior distribution of large data sets and high-dimensional parameter spaces. data sets and high-dimensional parameter space, making it suitable for Bayesian statistical inference.
A segmentation network is a type of neural network that can be used to identify different objects or regions in an image on a pixel-by-pixel basis and divide them into segments (regions). It is mainly used in computer vision tasks and plays an important role in many applications because it can associate each pixel in an image to a different class or category. This section provides an overview of this segmentation network and its implementation in various algorithms.
Labeling of image information can be achieved by various machine learning approaches, as described below. This time, we would like to consider the fusion of these machine learning approaches and the constraint satisfaction approach, which is a rule-based approach. These approaches can be extended to labeling text data using natural language processing, etc.
Support Vector Machine (SVM) is a supervised learning algorithm widely used in pattern recognition and machine learning. is to find the best separating hyperplane between the classes in the feature vector space, which is determined to have the maximum margin with the data points in the feature space. The margin is defined as the distance between the separation hyperplane and the nearest data point (support vector), and in SVM, the optimal separation hyperplane can be found by solving the margin maximization problem.
This section describes various practical examples of this support vector machine and their implementation in python.
LightGBM is a Gradient Boosting Machine (GBM) framework developed by Microsoft, which is a machine learning tool designed to build fast and accurate models for large data sets. Here we describe its implementation in pyhton, R, and Clojure.
Generalized Linear Model (GLM) is one of the statistical modeling and machine learning methods used for stochastic modeling of the relationship between response variables (objective variables) and explanatory variables (features). This section provides an overview of this generalized linear model and its implementation in various languages (python, R, and Clojure).
Time-series data is called data whose values change over time, such as stock prices, temperatures, and traffic volumes. By applying machine learning to this time series data, a large amount of data can be learned and used for business decision making and risk management by making predictions on unknown data. This section describes the implementation of time series data using python and R.
Time-series data is called data whose values change over time, such as stock prices, temperatures, and traffic volumes. By applying machine learning to this time-series data, a large amount of data can be learned and used for business decision making and risk management by making predictions on unknown data. In this article, we will focus on state-space models among these approaches.
- Overview of Kalman Filter Smoother and Examples of Algorithms and Implementations
Kalman Filter Smoother, a type of Kalman filtering, is a technique used to improve state estimation of time series data. The method usually models the state of a dynamic system and combines it with observed data for more precise state estimation.
- Dynamic Linear Model (DLM) Overview, Algorithm and Implementation Example
A Dynamic Linear Model (DLM) is a form of statistical modeling that accounts for temporal variation, and this model will be used to analyze time-series data and time-dependent data. Dynamic linear models are also referred to as linear state-space models.
- Overview of Constraint-Based Structural Learning and Examples of Algorithms and Implementations
Constraint-based structural learning is a method of learning models by introducing specific structural constraints in graphical models (e.g., Bayesian networks, Markov random fields, etc.), an approach that allows prior knowledge and domain knowledge to be incorporated into the model.
- BIC, BDe, and other score-based structural learning
Score-based structural learning methods such as BIC (Bayesian Information Criterion) and BDe (Bayesian Data Information Criterion) will be those used to evaluate the goodness of a model by combining the complexity of the statistical model and the goodness of fit of the data to select the optimal model structure. These methods are mainly based on Bayesian statistics and are widely used as information criteria for model selection.
- Bayesian Network Sampling (Sampling)
Bayesian network sampling models the stochastic behavior of unknown variables and parameters through the generation of random samples from the posterior distribution. Sampling is an important method in Bayesian statistics and probabilistic programming, and is used to estimate the posterior distribution of a Bayesian network and to evaluate uncertainty. It is an important method in Bayesian statistics and probabilistic programming, and is used to estimate the posterior distribution of Bayesian networks and to evaluate certainty.
- Variational Bayesian Analysis of Dynamic Bayesian Networks
A dynamic Bayesian network (DBN) is a type of Bayesian network for modeling uncertainty that changes over time. The variational Bayesian method is a statistical method for inference of complex probabilistic models, which allows estimating the posterior distribution based on uncertain information.
- Overview of Variational Autoencoder Bayes (Variational Autoencoder, VAE) and Examples of Algorithms and Implementations
Variational Autoencoder (VAE) is a type of generative model and a neural network architecture for learning latent representations of data. The VAE learns latent representations by modeling the probability distribution of the data and sampling from it. An overview of VAE is given below.
Diffusion Models are a class of generative models that perform well in tasks such as image generation and data repair. These models are generated by “diffusing” the original data in a series of steps.
DDIM (Diffusion Denoising Score Matching) is a method for removing noise from images. This approach uses a diffusion process to remove noise, combined with a statistical method called score matching. In this method, a noise image is first generated by adding random noise to the input image, and then the diffusion process is applied to these noise images as input to remove the noise by smoothing the image structure. Score matching is then used to learn the probability density function (PDF) of the noise-removed images. Score matching estimates the true data distribution by minimizing the difference between the gradient (score) of the denoised image and the gradient of the true data distribution, thereby more accurately recovering the true structure of the input image.
Denoising Diffusion Probabilistic Models (DDPMs) are probabilistic models used for tasks such as image generation and data completion, which model the distribution of images and data using a stochastic generative process.
- Overview of the Non-Maximum Suppression (NMS) Algorithm and Examples of Implementations
Non-Maximum Suppression (NMS) is an algorithm used in computer vision tasks such as object detection, mainly for selecting the most reliable one from multiple overlapping bounding boxes or detection windows. It will be.
Stable Diffusion is a method used in the field of machine learning and generative modeling, and is an extension of the Diffusion Models described in “Overview, Algorithms, and Examples of Implementations of Diffusion Models,” which are known generative models for images and audio. Diffusion Models are known for their high performance in image generation and restoration, and Stable Diffusion expands on this to enable higher quality and more stable generation.
- Overview of Bayesian Neural Networks and Examples of Algorithms and Implementations
Bayesian neural networks (BNNs) are architectures that integrate probabilistic elements into neural networks, whereas regular neural networks are deterministic, BNNs build probabilistic models based on Bayesian statistics. This allows the model to account for uncertainty and has been applied in a variety of machine learning tasks.
- Overview of Dynamic Bayesian Networks (DBN) and Examples of Algorithms and Implementations
Dynamic Bayesian Network (DBN) is a type of Bayesian Network (BN), which is a type of probabilistic graphical model used for modeling time-varying and serial data. DBN is a powerful tool for time series and dynamic data and has been applied in various fields.
SNAP is an open-source software library developed by the Computer Science Laboratory at Stanford University that provides tools and resources used in various network-related studies, including social network analysis, graph theory, and computer network analysis. The library provides tools and resources used in a variety of network-related research, including social network analysis, graph theory, and computer network analysis.
CDLib (Community Discovery Library) is a Python library that provides community detection algorithms, offering a variety of algorithms for identifying community structure in graph data and helping researchers and data scientists address different It will support researchers and data scientists in dealing with different community detection tasks.
MODULAR is one of the methods and tools used in the research areas of computer science and network science to solve multi-objective optimization problems of complex networks, the approach is designed to simultaneously optimize the structure and dynamics of the network, taking different objective functions ( multi-objective optimization) are taken into account.
The Louvain method (or Louvain algorithm) is one of the effective graph clustering algorithms for identifying communities (clusters) in a network. The Louvain method employs an approach that maximizes a measure called modularity to identify the structure of the communities.
Infomap (Information-Theoretic Modularity) is a community detection algorithm used to identify communities (modules) in a network. It focuses on optimizing the flow and structure of information.
Copra (Community Detection using Partial Memberships) is an algorithm and tool for community detection that takes into account the detection of communities in complex networks and the fact that a given node may belong to multiple communities. Copra is suitable for realistic scenarios where each node can belong to multiple communities using partial community membership information.
IsoRankN is one of the algorithms for network alignment, which is the problem of finding a mapping of corresponding vertices between different networks. IsoRankN is an improved version of the IsoRank algorithm that maps vertices between different networks with high accuracy and efficiency. IsoRankN aims to preserve similarity in different networks by mapping vertices taking into account their structure and characteristics.
- Overview of the Weisfeiler-Lehman Algorithm, Related Algorithms, and Examples of Implementations
The Weisfeiler-Lehman Algorithm (W-L Algorithm) is an algorithm for graph isomorphism testing and is primarily used to determine whether two given graphs are isomorphic.
Techniques for analyzing graph data that changes over time have been applied to a variety of applications, including social network analysis, web traffic analysis, bioinformatics, financial network modeling, and transportation system analysis. Here we provide an overview of this technique, its algorithms, and examples of implementations.
Snapshot Analysis (Snapshot Analysis) is a method of data analysis that takes into account changes over time by using snapshots of data at different time points (instantaneous data snapshots). This approach helps analyze data sets with information about time to understand temporal patterns, trends, and changes in that data, and when combined with Graphical Data Analysis, allows for a deeper understanding of temporal changes in network and relational data. This section provides an overview of this approach and examples of algorithms and implementations.
Dynamic Community Detection (Dynamic Community Analysis) will be a technique for tracking and analyzing temporal changes in communities (modules or clusters) within a network with time-relevant information (dynamic network). Usually targeting graph data (dynamic graphs) whose nodes and edges have time-related information, the method has been applied in various fields, e.g., social network analysis, bioinformatics, Internet traffic monitoring, financial network analysis, etc. It is used in the following areas.
Dynamic Centrality Metrics is a type of graph data analysis that takes into account changes over time. Usual centrality metrics (e.g., degree centrality, mediation centrality, eigenvector centrality, etc.) are suitable for static networks and It provides a single snapshot of the importance of a node. However, since real networks often have time-related elements, it is important to consider temporal changes in the network.
Dynamic module detection is a method of graph data analysis that takes time variation into account. This method tracks changes in communities (modules) in a dynamic network and identifies the community structure at different time snapshots. Here we present more information about dynamic module detection and an example implementation.
Dynamic Graph Embedding is a powerful technique for graph data analysis that takes temporal variation into account. This approach aims to have a representation of nodes and edges on a time axis when graph data varies along time.
Tensor decomposition (TD) is a method for approximating high-dimensional tensor data to low-rank tensors. This technique is used for data dimensionality reduction and feature extraction and is a useful approach in a variety of machine learning and data analysis applications. The application of tensor decomposition methods to dynamic module detection is relevant to tasks such as time series data and dynamic data module detection.
Network alignment is a technique for finding similarities between different networks or graphs and mapping them together. By applying network alignment to graph data analysis that takes into account temporal changes, it is possible to map graphs of different time snapshots and understand their changes.
Graph data analysis that takes into account changes over time using a time prediction model is used to understand temporal patterns, trends, and predictions in graphical data. This section discusses this approach in more detail.
Subsampling of large graph data reduces data size and controls computation and memory usage by randomly selecting portions of the graph, and is one technique to improve computational efficiency when dealing with large graph data sets. In this section, we discuss some key points and techniques for subsampling large graph data sets.
The Dynamic Factor Model (DFM) is one of the statistical models used in the analysis of multivariate time series data, which explains the variation of data by decomposing multiple time series variables into common factors (factors) and individual factors (specific factors). This is a model that explains data variation by decomposing multiple time series variables into common factors and individual factors (specific factors). This paper describes various algorithms and applications of DFM, as well as their implementations in R and Python.
Bayesian Structural Time Series Model (BSTS) is a type of statistical model that models phenomena that change over time and is used for forecasting and causal inference. This section provides an overview of BSTS and its various applications and implementations.
- Overview of Vector Autoregression Models and Examples of Applications and Implementations
Vector Autoregression Model (VAR model) is one of the time series data modeling methods used in fields such as statistics and economics, etc. VAR model is a model that is applied when multiple variables interact with each other. The general autoregression model (Autoregression Model) expresses the value of a variable as a linear combination of its past values, and the VAR model extends this idea to multiple variables, becoming a model that predicts current values using past values of multiple variables.
Online learning is a method of learning by sequentially updating a model in a situation where data arrives sequentially. Unlike batch learning in ordinary machine learning, this algorithm is characterized by the fact that the model is updated each time new data arrives. This section describes various algorithms and examples of applications of on-run learning, as well as examples of implementations in python.
Online Prediction (Online Prediction) is a technique that uses models to make predictions in real time under conditions where data arrive sequentially.” Online learning, as described in “Overview of Online Learning, Various Algorithms, Application Examples, and Specific Implementations,” is characterized by the fact that models are learned sequentially but the immediacy of model application is not clearly defined, whereas online prediction is characterized by the fact that predictions are made immediately upon the arrival of new data and the results are used. characteristic.
This section discusses various applications and specific implementation examples for this online forecasting.
Robust Principal Component Analysis (RPCA) is a method for finding a basis in data, and is characterized by its robustness to data containing outliers and noise. This paper describes various applications of RPCA and its concrete implementation using pyhton.
- About LLE (Locally Linear Embedding)
LLE (Locally Linear Embedding) is a nonlinear dimensionality reduction algorithm that embeds high-dimensional data into a lower dimension. It assumes that the data is locally linear and reduces the dimension while preserving the local structure of the data. It is primarily used for tasks such as clustering, data visualization, and feature extraction.
- About Multidimensional Scaling (MDS)
Multidimensional Scaling (MDS) is a statistical method for visualizing multivariate data that provides a way to place data points in a low-dimensional space (usually two or three dimensions) while preserving distances or similarities between the data. This technique is used to transform high-dimensional data into easily understandable low-dimensional plots that help visualize data features and clustering.
- About t-SNE (t-distributed Stochastic Neighbor Embedding)
t-SNE is a nonlinear dimensionality reduction algorithm that embeds high-dimensional data into lower dimensions. t-SNE is mainly used for tasks such as data visualization and clustering, where its particular strength is its ability to preserve the nonlinear structure of high-dimensional data. t-SNE’s main ideas are The main idea of t-SNE is to reflect the similarity of high-dimensional data in a low-dimensional space.
- About UMAP (Uniform Manifold Approximation and Projection)
UMAP is a nonlinear dimensionality reduction method for high-dimensional data, which aims to embed the data in a lower dimension while preserving its structure. It is used for visualization and clustering in the same way as t-SNE (t-distributed Stochastic Neighbor Embedding) described in “About t-SNE (t-distributed Stochastic Neighbor Embedding)” but adopts a different approach in some respects.
Natural Language Processing (NLP) is a generic term for technologies for processing human natural language on computers, with the goal of developing methods and algorithms for understanding, interpreting, and generating textual data.
This section describes the various algorithms used for this natural language processing, the libraries and platforms that implement them, and specific examples of their implementation in various applications (document classification, proper name recognition, summarization, language modeling, sentiment analysis, and question answering).
Natural language processing (NLP) preprocessing is the process of preparing text data into a form suitable for machine learning models and analysis algorithms. Since machine learning models and analysis algorithms cannot ensure high performance for all data, the selection of appropriate preprocessing is an important requirement for the success of NLP tasks. Typical NLP preprocessing methods are described below. These methods are generally performed on a trial-and-error basis based on the characteristics of the data and task.
The evaluation of text using natural language processing (NLP) is the process of quantitatively or qualitatively evaluating the quality and characteristics of textual data, a method that is relevant to a variety of NLP tasks and applications. This section describes various document evaluation sectoral methods.
Lexical learning using natural language processing (NLP) is the process by which a program understands the vocabulary of a language and learns the meaning and context of words. Lexical learning is the core of the NLP task, extracting the meaning of words and phrases from text data and enabling the model to understand natural language more effectively, an important It is a step in the process. This section provides an overview of this lexical learning, various algorithms and implementation examples.
Dealing with polysemous words (homonyms) in machine learning is one of the key challenges in tasks such as natural language processing (NLP) and information retrieval. Polysemy refers to cases where the same word has different meanings in different contexts, and various approaches exist to solve the problem of polysemy.
Multilingual NLP in machine learning is the field of developing natural language processing (NLP) models and applications for multiple languages, a key challenge in the field of machine learning and natural language processing, and a component of serving different cultural and linguistic communities. The NLP field is an important issue in the field of machine learning and natural language processing and is an element for serving different cultural and linguistic communities.
Language Detection algorithms are methods for automatically determining which language a given text is written in, and language detection is used in a variety of applications, including multilingual processing, natural language processing, web content classification, and machine translation preprocessing. Language detection is used in a variety of applications, including multilingual processing, natural language processing, web content classification, and machine translation preprocessing. This section describes common language detection algorithms and methods.
Translation models in machine learning are widely used in the field of natural language processing (NLP) and are designed to automate text translation from one language to another. These models use statistical methods and deep learning architectures to understand sentence structure and meaning and to perform translation.
Multilingual Embeddings is a technique for embedding text data in different languages into a vector space. This embedding represents the language information in the text data as a numerical vector and allows text in different languages to be placed in the same vector space, making multilingual embeddings a useful approach for natural language processing (NLP) tasks such as multilingual processing, translation, class classification, and sentiment analysis.
The Lesk algorithm is a method for determining the meaning of words in the field of natural language processing, and in particular, it is an approach used for Word Sense Disambiguation (WSD). Word sense disambiguation is the problem of selecting the correct meaning of a word when it has multiple different senses, depending on the context.
- Overview of the Aho-Hopcroft-Ullman Algorithm and Related Algorithms and Examples of Implementations
The Aho-Hopcroft-Ullman Algorithm (Aho-Hopcroft-Ullman Algorithm) is known as an efficient algorithm for string processing problems such as string search and pattern matching. This algorithm combines the basic data structures in string processing, Trie and Finite Automaton, to efficiently search for patterns in strings, and is mainly used for string matching, but also has applications in compilers, text search engines, and other It is mainly used for string matching, but has applications in a wide range of fields, including compilers and text search engines.
Subword-level tokenization is a natural language processing (NLP) approach that divides text data into subwords (parts of words) that are smaller than words. This is used to facilitate understanding of the meaning of sentences and to alleviate lexical constraints. There are several approaches to subword-level tokenization.
- User-Customized Learning Assistance with Natural Language Processing
User-customized learning aids utilizing natural language processing (NLP) are being offered in a variety of areas, including the education field and online learning platforms. This section describes the various algorithms used and their specific implementations.
- Overview of Automatic Summarization Technology and Examples of Algorithms and Implementations
Automatic summarization technology is widely used in information retrieval, information processing, natural language processing, machine learning, and other fields to compress large text documents and sentences into a short, to-the-point form that is easy to understand. This section provides an overview of this automatic summarization technology, various algorithms and implementation examples.
- About Monitoring and Supporting Online Discussions Using Natural Language Processing
Monitoring and supporting online discussions using Natural Language Processing (NLP) is used in online communities, forums, and social media platforms to improve the user experience, facilitate appropriate communication, and detect problems early. It is an approach that can be used to improve the user experience, facilitate appropriate communication, and detect problems early. This paper describes various algorithms and implementations of online discussion monitoring and support using natural language processing (NLP).
Relational Data Learning is a machine learning method for relational data (e.g., graphs, networks, tabular data, etc.). Conventional machine learning is usually applied only to individual instances (e.g., vectors or matrices), but relational data learning considers multiple instances and the relationships among them.
This section discusses various applications for this relational data learning and specific implementations in algorithms such as spectral clustering, matrix factorization, tensor decomposition, probabilistic block models, graph neural networks, graph convolutional networks, graph embedding, and metapath walks. The paper describes.
Structural Learning is a branch of machine learning that refers to methods for learning structures and relationships in data, usually in the framework of unsupervised or semi-supervised learning. Structural learning aims to identify and model patterns, relationships, or structures present in the data to reveal the hidden structure behind the data. Structural learning targets different types of data structures, such as graph structures, tree structures, and network structures.
This section discusses various applications and concrete implementations of structural learning.
A graph neural network (GNN) is a type of neural network for data with a graph structure. ) to express relationships between elements. Examples of graph-structured data include social networks, road networks, chemical molecular structures, and knowledge graphs.
This section provides an overview of GNNs and various examples and Python implementations.
Graph Convolutional Neural Networks (GCN) is a type of neural network that enables convolutional operations on data with a graph structure. While regular convolutional neural networks (CNNs) are effective for lattice-like data such as image data, GCNs were developed as a deep learning method for non-lattice-like data with very complex structures, such as graph data and network data.
Graph Embedding (Graph Embedding) is an approach that combines graph theory and machine learning by mapping the graph structure into a low-dimensional vector space, where the nodes and edges of the graph are represented by dense numerical vectors and processed by a machine learning algorithm. The purpose of graph embedding is to represent each node as a dense vector while preserving information about the graph structure, and this representation makes it possible to handle a wide variety of information. In addition, by using the distance between vectors instead of the distance between nodes conventionally represented by edges, the computational cost can be reduced, and parallel and distributed algorithms can be applied to tasks such as node classification, node clustering, graph visualization, and link prediction.
The encoder/decoder model is one of the key architectures in deep learning, which is structured to encode an input sequence into a fixed-length vector representation and then decode that representation to generate a target sequence. The encoder and decoder model in Graph Neural Networks (GNNs) provides a framework for learning feature representations (embeddings) from graph data and using those representations to solve various tasks on the graph.
Dynamic Graph Embedding is a technique for analyzing time-varying graph data, such as dynamic networks and time-varying graphs. While conventional embedding for static graphs focuses on obtaining a fixed representation of nodes, the goal of dynamic graph embedding is to obtain a representation that corresponds to temporal changes in the graph.
Spatio-Temporal Graph Convolutional Network (STGCN) is a convolution for time-series data on a graph consisting of nodes and edges. Recurrent Neural Network, RNN), which is a model used to predict time variation instead of a recurrent neural network (RNN). This is an effective approach for data where geographic location and temporal changes are important, such as traffic flow and weather data.
GNNs (Graph Neural Networks) are neural networks for handling graph-structured data, which use node and edge (vertex and edge) information to capture patterns and structures in graph data, and are applicable to social network analysis, chemical structure prediction, recommendation systems, graph It can be applied to social network analysis, chemical structure prediction, recommendation systems, graph-based anomaly detection, etc.
- Overview of Random Walks, Algorithms and Examples of Implementations
Random Walk is a basic concept used in graph theory and probability theory to describe random movement patterns in graphs and to help understand the structure and properties within a graph.
Message passing in machine learning is an effective approach to data and problems with graph structures, and is a widely used technique, especially in methods such as Graph Neural Networks (GNN).
ChebNet (Chebyshev network) is a type of Graph Neural Network (GNN), which is one of the main methods for performing convolution operations on graph-structured data. ChebNet is an approximate implementation of convolution operations on graphs using Chebyshev polynomials, which are used in signal processing.
DCNN is a type of Convolutional Neural Network (CNN), which is described in “Overview, Algorithm and Implementation Examples of CNN” for data structures such as images and graphs. (Graph Convolutional Neural Networks, GCN)” in “Overview, Algorithms, and Examples of Implementation. While ordinary CNN is effective when the data has a grid-like structure, it is difficult to apply it directly to graphs and atypical data, and GCN was developed as a deep learning method for non-grid-like data with very complex structures such as graph data and network data. DCNN applies the concept of the Diffusion Model described in “Overview of Diffusion Models, Algorithms, and Examples of Implementations” to GCN.
Graph Attention Network (GAT) is a deep learning model that uses an attention mechanism to learn the representation of nodes in a graph structure. GAT is a model that uses a set of mechanisms to learn the representation of a node.
- Graph Isomorphism Network (GIN) Overview, Algorithm and Example Implementation
Graph Isomorphism Network (GIN) is a neural network model for learning isomorphism of graph structures. The graph isomorphism problem is the problem of determining whether two graphs have the same structure, and is an important approach in many fields.
Dynamic Graph Neural Networks (D-GNN) are a type of Graph Neural Networks (GNN) designed to deal with dynamic graph data, where nodes and edges change with time. It is designed to handle data in which nodes and edges change over time. (For more information on GNNs, see “Graph Neural Networks: Overview, Applications, and Example Python Implementations. The approach has been used in a variety of domains including time series data, social network data, traffic network data, and biological network data.
MAGNA is a set of algorithms and tools for mapping different types of nodes (e.g., proteins and genes) in biological networks. This approach can be useful for identifying relationships between different types of biological entities.
TIME-SI (Time-aware Structural Identity) is one of the algorithms or methods for identifying structural correspondences between nodes in a network, taking into account time-related information. It will be used in a variety of network data, including social networks.
Diffusion Models for graph data is a method for modeling how information and influence spread over a network, and is used to understand and predict the propagation of influence and diffusion of information in social networks and network structured data. Below is a basic overview of Diffusion Models for graph data.
GRAAL (Graph Algorithm for Alignment of Networks) is an algorithm to align different network data, such as biological networks and social networks, and is mainly used for comparison and analysis of biological networks. GRAAL is designed to solve network mapping problems and identify common elements (nodes and edges) between different networks.
HubAlign (Hub-based Network Alignment) is an algorithm for mapping (alignment) between different networks, which is used to identify common elements (nodes and edges) between different networks. It is mainly used in areas such as bioinformatics and social network analysis.
IsoRank (Isomorphism Ranking) is an algorithm for aligning different networks, which uses network isomorphism (graph isomorphism) to calculate the similarity between two different networks and estimate the correspondence of nodes based on it. IsoRank is used in areas such as data integration between different networks, network comparison, bioinformatics, and social network analysis.
- ST-GCN (Spatio-Temporal Graph Convolutional Networks) Overview, Algorithm and Examples of Implementation
ST-GCNs (Spatio-Temporal Graph Convolutional Networks) are a type of graph convolutional networks designed to handle video and temporal data. data), this method can perform feature extraction and classification by considering both spatial information (relationships between nodes in the graph) and temporal information (consecutive frames or time steps). It is primarily used for tasks such as video classification, motion recognition, and sports analysis.
- Overview of DynamicTriad and Examples of Algorithms and Implementations
DynamicTriad is a method for modeling temporal changes in dynamic graph data and predicting node correspondences. This approach has been applied to predicting correspondences in dynamic networks and understanding temporal changes in nodes.
VERSE (Vector Space Representations of Graphs) is one of the methods for learning to embed graph data. By embedding graph data in a low-dimensional vector space, it quantifies the characteristics of nodes and edges and provides a representation to be applied to machine learning algorithms. VERSE is known for its ability to learn fast and effective embeddings, especially for large graphs.
- GraphWave Overview, Algorithm, and Example Implementation
GraphWave is a method for learning graph data embedding, a technique for converting node and edge features into low-dimensional vectors that can be useful for applying graph data to machine learning algorithms. GraphWave is a unique approach that can learn effective embeddings by considering the graph structure and surrounding information.
- Overview of LINE (Large-scale Information Network Embedding), Algorithm, and Implementation Examples
LINE (Large-scale Information Network Embedding) is a graph data algorithm for efficiently embedding large-scale information networks (graphs). LINE aims to learn feature vectors (embeddings) of nodes (a node represents an individual element or entity in a graph), which can then be used to capture relationships and similarities among nodes and applied to various tasks.
Node2Vec is an algorithm for effectively embedding nodes in graph data. Node2Vec is based on similar ideas to Word2Vec and uses random walks to learn to embed nodes. This algorithm captures the similarity and relatedness of nodes and has been applied to different graph data related tasks.
GraREP (Graph Random Neural Networks for Representation Learning) is a new deep learning model for graph representation learning. Graph representation learning is the task of learning the representation of each element (node or edge) from graph structure data consisting of nodes and edges, and plays an important role in various fields such as social networks, molecular structures, and communication networks.
Structural Deep Network Embedding (SDNE) is a type of graph autoencoder that extends autoencoders to graphs. An autoencoder is a neural network that performs unsupervised learning to encode given data into a low-dimensional vector in a latent space. Among them, SDNE is a multi-layer autoencoder (Stacked AutoEncoder) that aims to maintain first-order and second-order proximity simultaneously.
- Overview of MODA (MOdule Detection in Dynamic Networks Algorithm) and Examples of Implementations
MODA is an algorithm for detecting modules (groups of nodes) in dynamic network data. MODA will be designed to take into account changes over time and to be able to track how modules in a network evolve. The algorithm has been useful in a variety of applications, including analysis of dynamic networks, community detection, and evolution studies.
- DynamicTriad Overview, Algorithm and Implementation Examples
DynamicTriad is one of the models used in the field of Social Network Analysis (SNA), a method to study the relationships among people, organizations, and other elements and to understand their network structure and characteristics. Network Analysis Using Clojure (2) Calculating Triads in a Graph Using Glittering”, DynamicTriad is a tool for understanding the evolution of an entire network by tracking changes in a triad (set of triads) consisting of three elements. This approach allows for a more comprehensive analysis of the network, since it can take into account not only the individual relationships within the network, but also the movements of groups and subgroups.
- Overview of DANMF (Dynamic Attributed Network with Matrix Factorization) and Examples of Implementations
DANMF (Dynamic Attributed Network with Matrix Factorization) is one of the graph embedding methods for network data with dynamic attribute information. The method learns to embed nodes by combining node attribute information with the network structure. This method is particularly useful when dynamic attribute information is included, and is suitable when node attributes change with time or when different attribute information is available at different time steps.
GraphSAGE (Graph Sample and Aggregated Embeddings) is one of the graph embedding algorithms for learning node embeddings (vector representation) from graph data. By sampling and aggregating the local neighborhood information of nodes, it effectively learns the embedding of each node. This approach makes it possible to obtain high-performance embeddings for large graphs.
Variational Graph Auto-Encoders (VGAE) is a type of VAE described in “Overview, Algorithms, and Examples of Variational Autoencoder (VAE)” for graph data.
DeepWalk is a machine learning algorithm for graph data analysis, particularly suited for a task called node representation learning (Node Embedding), a method aimed at embedding nodes into a low-dimensional vector space, where nodes share with their neighbors in the graph DeepWalk has been used in a variety of applications, including social networks, web page link graphs, and collaborative filtering.
- Overview of the Girvan-Newman Algorithm and Examples of Implementations
The Girvan-Newman algorithm is an algorithm for detecting the community structure of a network in graph theory, By removing these edges, the network is partitioned into communities.
Bayesian deep learning refers to an attempt to incorporate the principles of Bayesian statistics into deep learning. In ordinary deep learning, model parameters are treated as non-probabilistic values, and optimization algorithms are used to find optimal parameters. This is called “Bayesian deep learning”. For more information on the application of uncertainty to machine learning, please refer to “Uncertainty and Machine Learning Techniques” and “Overview of Statistical Learning Theory (Non-Equationary Explanation).
- Black-Box Variational Inference (BBVI) Overview, Algorithm, and Implementation Examples
Black-Box Variational Inference (BBVI) is a type of variational inference method for approximating the posterior distribution of complex probabilistic models in probabilistic programming and Bayesian statistical modeling. BBVI is called “Black-Box” because the probability model to be inferred is treated as a black box and can be applied independently of the internal structure of the model itself and the form of the likelihood function. BBVI is a method that can be used for inference without knowing the internal structure of the model.
A knowledge graph is a graph structure that represents information as a set of related nodes (vertices) and edges (connections), and is a data structure used to connect information on different subjects or domains and visualize their relationships. This paper outlines various methods for automatic generation of this knowledge graph and describes specific implementations in python.
A knowledge graph is a graph structure that represents information as a set of related nodes (vertices) and edges (connections), and is a data structure used to connect information on different subjects or domains and visualize their relationships. This section describes various applications of the knowledge graph and concrete examples of its implementation in python.
- General Problem Solver and Application Examples, Implementation Examples in LISP and Python
The general problem solver specifically takes as input the description of the problem and constraints, and operates to execute algorithms to find an optimal or valid solution. These algorithms vary depending on the nature and constraints of the problem, and there are a variety of general problem-solving methods, including numerical optimization, constraint satisfaction, machine learning, and search algorithms. This section describes examples of implementations in LISP and Python for this GPS.
- Directed Acyclic Graphs and Blockchain Technology
Directed Acyclic Graph (DAG) is a graph data algorithm that appears in various situations such as automatic management of various tasks and compilers. In this article, I would like to discuss DAGs.
Uncertainty (Uncertainty) refers to a state of uncertainty or information in which future events or outcomes are difficult to predict, caused by the limitations of our knowledge or information, and represents a state in which it is difficult to have complete information or certainty. Mathematical methods and models, such as probability theory and statistics, are used to deal with uncertainty. These methods are important tools for quantifying uncertainty and minimizing risk.
This section describes probability theory and various implementations for handling this uncertainty.
Bayesian inference is a method of statistical inference based on a probabilistic framework and is a machine learning technique for dealing with uncertainty. The objective of Bayesian inference is to estimate the probability distribution of unknown parameters by combining data and prior knowledge (prior distribution). This paper provides an overview of Bayesian estimation, its applications, and various implementations.
- Bayesian Network Inference Algorithms
Bayesian network inference is the process of finding the posterior distribution based on Bayes’ theorem, and there are several types of major inference algorithms. The following is a description of typical Bayesian network inference algorithms.
- Overview of Bayesian Multivariate Statistical Modeling and Examples of Algorithms and Implementations
Bayesian multivariate statistical modeling is a method of simultaneously modeling multiple variables (multivariates) using a Bayesian statistical framework, which allows the method to capture the probabilistic structure and account for uncertainty with respect to the observed data. Multivariate statistical modeling is used to address issues such as data correlation, covariance structure, and outlier detection.
- Dirichlet Process Mixture Model (DPMM) Overview, Algorithm and Implementation Examples
The Dirichlet Process Mixture Model (DPMM) is one of the most important models in clustering and cluster analysis. The DPMM is characterized by its ability to automatically estimate clusters from data without the need to determine the number of clusters in advance.
Markov Chain Monte Carlo (MCMC) is a statistical method for sampling from probability distributions and performing integration calculations. The MCMC is a combination of a Markov Chain and a Monte Carlo method. This section describes various algorithms, applications, and implementations of MCMC.
- Overview of NUTS and Examples of Algorithms and Implementations
NUTS (No-U-Turn Sampler) is a type of Hamiltonian Monte Carlo (HMC) method, which is an efficient algorithm for sampling from probability distributions, as described in “MCMC Method for Stochastic Integral Calculations: Algorithms other than Metropolis Method (HMC Method)”. HMC is based on the Hamiltonian dynamics of physics and is a type of Markov chain Monte Carlo method. NUTS improves on the HMC method by automatically selecting the appropriate step size and sampling direction to achieve efficient sampling.
A topic model is a statistical model for automatically extracting topics (themes or categories) from large amounts of text data. Examples of text data here include news articles, blog posts, tweets, and customer reviews. The topic model is a principle that analyzes the pattern of word occurrences in the data to estimate the existence of topics and the relevance of each word to the topic.
This section provides an overview of this topic model and various implementations (topic extraction from documents, social media analysis, recommendations, topic extraction from image information, and topic extraction from music information), mainly using the python library.
Variational methods (Variational Methods) are used to find the optimal solution in a function or probability distribution, and are one of the optimization methods widely used in machine learning and statistics, especially in stochastic generative models and variational autoencoders (VAE). In particular, it plays an important role in machine learning models such as stochastic generative models and variational autoencoders (VAE).
Variational Bayesian Inference is one of the probabilistic modeling methods in Bayesian statistics, and is used when the posterior distribution is difficult to obtain analytically or computationally expensive.
This section provides an overview of the various algorithms for this variational Bayesian learning and their python implementations in topic models, Bayesian regression, mixture models, and Bayesian neural networks.
HMM is a type of probabilistic model used to represent the process of generating a series of observations, and is widely used for modeling series data and time series data in particular. The hidden state represents the latent state behind the series data, which is not directly observed, while the observation results are the data that can be directly observed and generated from the hidden state.
This section describes various algorithms and practical examples of HMMs, as well as a concrete implementation in python.
- Overview of the Gelman-Rubin Statistic and Related Algorithms and Examples of Implementations
The Gelman-Rubin statistic (or Gelman-Rubin diagnostic, Gelman-Rubin statistical test) is a statistical method for diagnosing convergence of Markov chain Monte Carlo (MCMC) sampling methods, particularly when MCMC sampling is done with multiple chains, where each chain will be used to evaluate whether they are sampled from the same distribution. This technique is often used in the context of Bayesian statistics. Specifically, the Gelman-Rubin statistic evaluates the ratio between the variability of samples from multiple MCMC chains and the variability within each chain, and this ratio will be close to 1 if statistical convergence is achieved.
An image recognition system will be a technology in which a computer analyzes images and automatically identifies objects and features contained in them. This system is implemented by combining various artificial intelligence algorithms and methods, such as image processing, pattern recognition, machine learning, and deep learning. This section describes the steps for building this image recognition system and their specific implementation.
In image information processing, preprocessing has a significant impact on model performance and convergence speed, and is an important step in converting image data into a form suitable for the model. The following describes preprocessing methods for image information processing.
Object detection technology involves the automatic detection of specific objects or objects in an image or video and their location. Object detection is an important application of computer vision and image processing and is applied to many real-world problems. This section describes various algorithms and implementation examples for this object detection technique.
Haar Cascades is a feature-based algorithm for object detection, and Haar Cascades is widely used for computer vision tasks, especially face detection. This section provides an overview of this Haar Cascades and its algorithm and implementation.
Histogram of Oriented Gradients (HOG) is a feature extraction method used for object detection and recognition in the fields of computer vision and image processing. The principle of HOG is to capture information on edges and gradient directions in an image and represent object features based on this information. This section provides an overview of HOG, its challenges, various algorithms, and implementation examples.
Cascade Classifier is one of the pattern recognition algorithms used in object detection tasks. Cascade classifiers have been developed to achieve fast object detection, and in particular, the Haar Cascades form is widely known and used mainly for tasks such as face detection. This section provides an overview of this cascade classifier, its algorithms, and examples of implementations.
Contrastive Predictive Coding (CPC) is a representation learning technique used to learn semantically important representations from audio and image data. This method is a form of unsupervised learning, in which representations are learned by contrasting different observations in the training data.
R-CNN (Region-based Convolutional Neural Networks) is an approach to utilize deep learning in object detection tasks. neural networks (CNNs) to predict object classes and bounding boxes, and R-CNNs have shown very good performance in object detection tasks. This paper describes an overview of this R-CNN, its algorithm and implementation examples.
Faster Region-based Convolutional Neural Networks (Faster R-CNN) is one of a series of deep learning models that provide fast and accurate results in object detection tasks. Convolutional Neural Networks (R-CNNs)), and represents a major advance in the field of object detection, solving the problems of previous architectures called R-CNNs. This section provides an overview of this Faster R-CNN, its algorithms, and examples of implementations.
YOLO (You Only Look Once) is a deep learning-based algorithm for real-time object detection tasks. YOLO will be one of the most popular models in the fields of computer vision and artificial intelligence.
SSD (Single Shot MultiBox Detector) is one of the deep learning based algorithms for object detection tasks.
Mask R-CNN (Mask Region-based Convolutional Neural Network) is a deep learning-based architecture for object detection and object segmentation (instance segmentation), in which the location of each object is not only enclosed in a bounding box It has the ability to segment objects at the pixel level within an object as well as surround it, making it a powerful model for combining object detection and segmentation.
EfficientDet will be one of the computer vision models with high performance in the object detection task; EfficientDet is designed to balance the efficiency and accuracy of the model, and will provide superior performance with less computational resources.
RetinaNet is a deep learning-based architecture that performs well in object detection tasks by predicting the location of object bounding boxes and simultaneously estimating the probability of belonging to each object class. This architecture is based on an approach known as Single Shot Detector (SSD), which is also described in “Overview of SSD (Single Shot MultiBox Detector), Algorithms, and Examples of Implementations,” but it is more suitable for finding smaller or more difficult objects than a typical SSD. However, it performs better than the general SSD in detecting small or difficult-to-find objects.
Anchor Boxes and high Intersection over Union (IoU) thresholds play an important role in the object detection task of image recognition. The following sections discuss adjustments related to these elements and the detection of dense objects.
EfficientNet is one of the lightweight and efficient deep learning models and convolutional neural network (CNN) architectures.EfficientNet was proposed by Tan and Le in 2019 and was designed to optimize model size and It will be designed to achieve high accuracy while optimizing computational resources.
LeNet-5 (LeNet-5) is one of the most important historical neural network models in the field of deep learning and was proposed in 1998 by Yann Lecun, a pioneer in convolutional neural networks (CNN), as described in “CNN Overview and Algorithm and Implementation Examples. LeNet-5 was very successful in the handwritten digit recognition task and has contributed to the subsequent development of CNNs.
MobileNet is one of the most widely used deep learning models in the field of computer vision, and is a lightweight and efficient convolutional neural network (CNN) optimized for mobile devices developed by Google, as described in “CNN Overview, Algorithms and Implementation Examples”. MobileNet can be used for tasks such as image classification, object detection, and semantic segmentation, and offers superior performance, especially on resource-constrained devices and applications. It offers superior performance.
SqueezeNet is a lightweight, compact deep learning model and architecture for convolutional neural networks (CNNs), as described in “CNN Overview, Algorithms, and Implementation Examples. neural networks with small file sizes and low computational complexity, and is primarily suited for resource-constrained environments and devices.
A speech recognition system (Speech Recognition System) is a technology that converts human speech into a form that can be understood by a computer. This section describes the procedure for building a speech recognition system, and also describes a concrete implementation using python.
- Preprocessing for speech recognition processing
Pre-processing for speech recognition is the step of converting speech data into a format that can be input into a model and effectively perform learning and inference, and requires the following pre-processing methods.
Anomaly detection is a technique for detecting anomalous behavior or patterns in a data set or system. Anomaly detection is a system for modeling the behavior and patterns of normal data and detecting anomalies by evaluating deviations from them. Anomaly refers to the unexpected appearance of data or abnormal behavior, and is captured as differences or outliers from normal data. Anomaly detection is performed using both supervised and unsupervised learning methods.
This section provides an overview of anomaly detection techniques, application examples, and implementations of statistical anomaly detection, supervised anomaly detection, unsupervised anomaly detection, and deep learning-based anomaly detection.
Change detection technology (Change Detection) is a method for detecting changes or anomalies in the state of data or systems. Change detection compares two states, the learning period (past data) and the test period (current data), to detect changes in the state of the data or system. The mechanism is to model normal conditions and patterns using data from the learning period and compare them with data from the test period to detect abnormalities and changes.
This section provides an overview of this change detection technology, application examples, and specific implementations of the reference model, statistical change detection, machine learning-based change detection, and sequence-based change detection.
Causal inference is a methodology for inferring whether one event or phenomenon is a cause of another event or phenomenon. Causal exploration is the process of analyzing data and searching for potential causal candidates in order to identify causal relationships.
This section discusses various applications of causal inference and causal exploration, as well as a time-lag example.
Causal Forest is a machine learning model for estimating causal effects from observed data, based on Random Forest and extended based on conditions necessary for causal inference. This section provides an overview of the Causal Forest, application examples, and implementations in R and Python.
- Doubly Robust Learners (Doubly Robust Learners) Overview, Application Examples, and Examples of Python Implementations
Doubly Robust Learners is a statistical method used in the context of causal inference, which aims to obtain more robust results by combining two estimation methods when estimating causal effects from observed data. Here we provide an overview of Doubly Robust Learners, its algorithm, application examples, and a Python implementation.
Game theory is a theory for determining the optimal strategy when there are multiple decision makers (players) who influence each other, such as in competition or cooperation, by mathematically modeling their strategies and their outcomes. It is used primarily in economics, social sciences, and political science.
Various methods are used as algorithms for game theory, including minimax methods, Monte Carlo tree search, deep learning, and reinforcement learning. Here we describe examples of implementations in R, Python, and Clojure.
Explainable Machine Learning (EML) refers to methods and approaches that explain the predictions and decision-making results of machine learning models in an understandable way. In many real-world tasks, model explainability is often important. This can be seen, for example, in solutions for finance, where it is necessary to explain on which factors the model bases its credit score decisions, or in solutions for medical diagnostics, where it is important to explain the basis and reasons for predictions for patients.
In this section, we discuss various algorithms and examples of python implementations for this explainable machine learning.
Submodular optimization is a type of combinatorial optimization that solves the problem of maximizing or minimizing a submodular function, a function with specific properties. This section describes various algorithms, their applications, and their implementations for submodular optimization.
Mixed integer optimization is a type of mathematical optimization and refers to problems that simultaneously deal with continuous and integer variables. The goal of mixed integer optimization is to find optimal values of variables under constraints when maximizing or minimizing an objective function. This section describes various algorithms and implementations for this mixed integer optimization.
Particle Swarm Optimization (PSO) is a type of evolutionary computation algorithm inspired by swarming behavior in nature, modeling the behavior of flocks of birds and fish. PSO is characterized by its ability to search a wider search space than genetic algorithms, which tend to fall into local solutions. PSO is widely used to solve machine learning and optimization problems, and numerous studies and practical examples have been reported.
Case-based reasoning is a technique for finding appropriate solutions to similar problems by referring to past problem-solving experience and case studies. This section provides an overview of this case-based reasoning technique, its challenges, and various implementations.
Stochastic optimization represents a method for solving optimization problems involving stochastic elements, and stochastic optimization in machine learning is a widely used method for optimizing the parameters of a model. Whereas in general optimization problems, the goal is to find optimal values of parameters to minimize or maximize the objective function, stochastic optimization is particularly useful when the objective function contains noise or randomness caused by various factors, such as data variability or observation error .
In stochastic optimization, random factors and stochastic algorithms are used to find the optimal solution. For example, in the field of machine learning, stochastic optimization methods are frequently used to optimize parameters such as weights and biases of neural networks. In SGD (Stochastic Gradient Descent), a typical method, optimization is performed by randomly selecting samples of the data set and updating parameters based on those samples, so that the model can be efficiently trained without using the entire data set The model can be trained without using the entire dataset.
This section describes implementations in python for SGD and mini-batch gradient descent, Adam, genetic algorithms, and Monte Carlo methods and examples of their application to parameter tuning, feature selection and dimensionality reduction, and k-means.
Multi-Task Learning is a machine learning method that simultaneously learns multiple related tasks. Usually, each task has a different data set and objective function, but Multi-Task Learning aims to incorporate these tasks into a model at the same time so that they can complement each other by utilizing their mutual relevance and shared information.
Here, we provide an overview of methods such as shared parameter models, model distillation, transfer learning, and multi-objective optimization for this multitasking, and discuss examples of applications in natural language processing, image recognition, speech recognition, and medical diagnosis, as well as a simple implementation in python.
Sparse modeling is a technique that takes advantage of sparsity in the representation of signals and data. Sparsity refers to the property that non-zero elements in data or signals are limited to a very small portion. The purpose of sparse modeling is to efficiently represent data by utilizing sparsity, and to perform tasks such as noise removal, feature selection, and compression.
This section provides an overview of sparse modeling algorithms such as Lasso, compression estimation, Ridge regularization, elastic nets, Fused Lasso, group regularization, message propagation algorithms, dictionary learning, etc., as well as a description of the various algorithms used in image processing, natural language processing, recommendation, signal processing The paper describes the implementation of the algorithms in various applications such as image processing, natural language processing, recommendation, machine learning, signal processing, brain science, and so on.
- Overview of Overlapping Group Regularization and Implementation Examples
Overlapping group regularization (Overlapping Group Lasso) is a type of regularization method used in machine learning and statistical modeling for feature selection and estimation of model coefficients. In this case, the feature is allowed to belong to more than one group at the same time. This section provides an overview of this overlapping group regularization and various implementations.
The Bandit problem is a type of reinforcement learning problem in which a decision-making agent learns which action to choose in an unknown environment. The goal of this problem is to find a method for selecting the optimal action among multiple actions.
In this section, we provide an overview and implementation of the main algorithms for this bandit problem, including the ε-Greedy method, UCB algorithm, Thompson sampling, softmax selection, substitution rule method, and Exp3 algorithm, as well as examples of their application to online advertising distribution, drug discovery, and stock investment, The paper also describes application examples such as online advertisement distribution, drug discovery, stock investment, and clinical trial optimization, and their implementation procedures.
The Multi-Armed Bandit Problem is a type of decision-making problem that involves finding the most rewarding option among multiple alternatives (arms), and this problem is used in real-time decision-making and applications that deal with trade-offs between search and exploitation This problem is used in the following applications.
- Count-Based Multi-Armed Bandit Problem Approach
The Count-Based Multi-Armed Bandit Problem is a type of reinforcement learning problem in which the distribution of rewards for each arm is assumed to be unknown in the context of obtaining rewards from different actions (arms). The main goal is to find a strategy (policy) that maximizes the rewards obtained by arm selection.
Contextual bandit is a type of reinforcement learning and a framework for solving the problem of making the best choice among multiple alternatives. The contextual bandit problem consists of the following elements. This section describes various algorithms for the contextual bandit and an example implementation in python.
- EXP3 (Exponential-weight algorithm for Exploration and Exploitation) Algorithm Overview and Implementation Example
EXP3 (Exponential-weight algorithm for Exploration and Exploitation) is one of the algorithms in the Multi-Armed Bandit Problem. EXP3 aims to find the optimal arm in such a situation while balancing the trade-off between exploration and exploitation. EXP3 aims to find the optimal arm while balancing the trade-off between Exploration and Exploitation.
Simulation involves modeling a real-world system or process and executing it virtually on a computer. Simulations are used in a variety of domains, such as physical phenomena, economic models, traffic flows, and climate patterns, and can be built in steps that include defining the model, setting initial conditions, changing parameters, running the simulation, and analyzing the results. Simulation and machine learning are different approaches, but they can interact in various ways depending on their purpose and role.
This section describes examples of adaptations and various implementations of this combination of simulation and machine learning.
- python and algorithms
- Machine Learning with python
- Statistical modeling with python
- Optimization methods with python
- Stopword removal in Clojure Stopword removal in python
- GPy – A Framework for Gaussian Processes Using Python
In this article, I will describe a framework for Gaussian processes using Python. There are two types of Python frameworks: one is based on the general-purpose scikit-learn framework, and the other is a dedicated framework, GPy. GPy is more versatile than scikit-learn, so we will focus on GPy in this article.
In the area of machine learning, environments with rich libraries such as Python and R are used and have become almost de facto. However, it was not at a level where the user could freely use the libraries of the other party, and there were hurdles in making full use of the latest algorithms.In contrast, in recent years (since 2018), frameworks that can interoperate with the Python environment, such as libPython-clj, have appeared, and mathematical frameworks that utilize Java and C libraries, such as fastmath, deep learning framework Cortex, Deep The development of frameworks such as fastmath, a mathematical framework that leverages Java and C libraries, and deep learning frameworks such as Cortex and DeepDiamond have led to active discussions on approaches to machine learning, such as scicloj.ml, a well-known machine learning community on Clojure.
Deep Learning
PyTorch is a deep learning library developed by Facebook and provided as open source. It has features such as flexibility, dynamic computation graphs, and GPU acceleration, making it possible to implement a variety of machine learning tasks. Below we describe various examples of implementations using PyTorch.
Adversarial attack is one of the most widely used attacks against machine learning models, especially for input data such as images, text, and voice. Adversarial attacks aim to cause misrecognition of machine learning models by applying slight perturbations (noise or manipulations). Such attacks can reveal security vulnerabilities and help assess model robustness
Conditional Generative Models are a type of generative model that has the ability to generate data given certain conditions. Conditional Generative Models play an important role in many application fields because they can generate data based on given conditions. This section describes various algorithms and concrete implementations of this conditional generative model.
Prompt Engineering” refers to techniques and methods used in the development of natural language processing and machine learning models to devise a given text prompt (instruction) and elicit the best response for a particular task or purpose. This is a particularly useful approach when using large-scale language models such as OpenAI’s GPT (Generative Pre-trained Transformer). The basic idea behind prompt engineering is to obtain better results by providing appropriate questions or instructions to the model. The prompts serve as input to the model, and their selection and expression affect the output of the model.
LangChain is a library that helps develop applications using language models and provides a platform on which various applications using ChatGPT and other generative models can be built. One of the goals of LangChain is to enable it to handle tasks that language models cannot, such as answering questions about information outside the scope of knowledge learned by language models, or tasks that are logically complex or computationally demanding, etc. Another is to maintain it as a framework.
This section continues the discussion of LangChain, as described in “Overview of ChatGPT and LangChain and its use”. In the previous article, we described ChatGPT and LangChain, a framework for using ChatGPT and LangChain. This time, I would like to describe Agent, which has the ability to autonomously interfere with the outside world and transcend the limits of language models.
Fine tuning of large-scale language models is the process of performing additional training on models that have been previously trained on a large data set, with the goal of enabling general-purpose models to be applied to specific tasks and domains to improve accuracy and performance.
LoRA (Low-Rank Adaptation) is a technique related to the fine tuning of large pre-trained models (LLMs), and was published in 2021 by Edward Hu et al. at Microsoft in their paper “LoRA: Low-Rank Adaptation of LoRA: Low-Rank Adaptation of Large Language Models” by Edward Hu et al.
Dense Passage Retrieval (DPR) is one of the retrieval techniques used in the field of Natural Language Processing (NLP). DPR will be specifically designed to retrieve information from large sources and find the best answers to questions about those sources.
The basic structure of RAG is to vectorize input queries with Query Encoder, find Documnet with similar vectors, and generate responses using those vectors. The vector DB is used to store the vectorized documents and to search for similar documents. Among these functions, as described in “Overview of ChatGPT and LangChain and their use”, ChatGPT’s API or LanChain is generally used for generative AI, and “Overview of Vector Database” is generally used for database. The database is generally described in “Overview of Vector Databases”. In this article, we describe a concrete implementation using these databases.
Huggingface is an open source platform and library for machine learning and natural language processing (NLP). The tools and resources provided by Huggingface are supported by an open source community, where there is an active effort to share code and models. This section describes the Huggingface Transformers, documentation generation, and implementation in python.
Attention in deep learning is an important concept used as part of neural networks. The Attention mechanism refers to the ability of a model to assign different levels of importance to different parts of the input, and the application of this mechanism has recently been recognized as being particularly useful in tasks such as natural language processing and image recognition.
This paper provides an overview of the Attention mechanism without using mathematical formulas and an example of its implementation in pyhton.
- Introducing the python development environment and tensflow package on mac
- Comparing tensorflow, Keras, and pytorch
A comparison is made between tensorflow, Kreas and pyhorch, which are open source frameworks for deep learning.
This section provides an overview of python Keras and examples of its application to basic deep learning tasks (handwriting recognition using MINIST, Autoencoder, CNN, RNN, LSTM).
The Seq2Seq (Sequence-to-Sequence) model is a deep learning model for taking sequence data as input and outputting sequence data, and in particular, it is an approach that can handle input and output sequences of different lengths. and dialogue systems, and is widely used in a variety of natural language processing tasks.
RNN (Recurrent Neural Network) is a type of neural network for modeling time-series and sequence data, and can retain past information and combine it with new information, such as speech recognition, natural language processing, video analysis, and time series prediction, It is a widely used approach for a variety of tasks.
LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN), which is a very effective deep learning model mainly for time series data and natural language processing (NLP) tasks. LSTM can retain historical information and model long-term dependencies, making it a suitable method for learning long-term information as well as short-term information.
- Overview of Bidirectional LSTM and Examples of Algorithms and Implementations
Bidirectional LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) that is widely used for modeling sequence data such as time series data and natural language processing. Bidirectional LSTM is characterized by its ability to simultaneously learn sequence data from the past to the future direction and to capture the context of the sequence data more richly.
- About GRU (Gated Recurrent Unit)
GRU (Gated Recurrent Unit) is a type of recurrent neural network (RNN) that is widely used in deep learning models, especially for processing time series data and sequence data. The GRU is designed to model long-term dependencies in the same way as the LSTM (Long Short-Term Memory) described in “Overview of LSTM and Examples of Algorithms and Implementations,” but it is characterized by its lower computational cost than the LSTM. It is characterized by lower computational cost than LSTM.
- About Bidirectional RNN (BRNN)
Bidirectional Recurrent Neural Network (BRNN) is a type of recurrent neural network (RNN) model that can simultaneously consider past and future information. BRNN is particularly useful for processing sequence data and is widely used in tasks such as natural language processing and It is widely used in tasks such as natural language processing and speech recognition.
- About Deep RNN
Deep RNN (Deep Recurrent Neural Network) is a type of recurrent neural network (RNN), which is a stacked model of multiple RNN layers. deep RNN helps model complex relationships in sequence data and extract more sophisticated feature representations. Typically, a Deep RNN consists of RNN layers stacked in multiple layers in the temporal direction.
- About Stacked RNN
Stacked RNN (Stacked Recurrent Neural Network) is a type of recurrent neural network (RNN) architecture that uses multiple RNN layers stacked on top of each other, enabling modeling of more complex sequence data and effectively capturing long-term dependencies It is a method that allows for more complex sequence data modeling and the ability to effectively capture long-term dependencies.
- About Echo State Network (ESN)
Echo State Network (ESN) is a type of reservoir computing, a type of recurrent neural network (RNN) used for prediction, analysis, and pattern recognition of time series and sequence data. tasks and may perform well in a variety of tasks.
- Overview of Pointer-Generator Networks, Algorithms, and Examples of Implementations
The Pointer-Generator network is a type of deep learning model used in natural language processing (NLP) tasks, and is particularly suited for tasks such as abstract sentence generation, summarization, and information extraction from documents. The network is characterized by its ability to copy portions of text from the original document verbatim when generating sentences.
CNN (Convolutional Neural Network) is a deep learning model mainly used for computer vision tasks such as image recognition, pattern recognition, and image generation. This section provides an overview of CNNs and implementation examples.
DenseNet (Densely Connected Convolutional Network) was proposed in 2017 by Gao Huang, Zhuang Liu, Kilian Q. Weinberger, and Laurens van der Maaten in “Overview of CNN DenseNet improves the efficiency of deep network training by introducing “dense” connections during convolutional neural network training, and reduces the gradient loss problem. and reducing the gradient loss problem.
ResNet is a deep convolutional neural network (CNN) architecture proposed by Kaiming He et al. in 2015, as described in “CNN Overview, Algorithms and Implementation Examples”. ResNet introduces innovative ideas and approaches that have achieved phenomenal performance in computer vision tasks.
GoogLeNet is a convolutional neural network (CNN) architecture described in Google’s 2014 “CNN Overview and Algorithms and Examples of Implementations”. This model achieved state-of-the-art performance in computer vision tasks such as the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), and GoogLeNet is known for its unique architecture and modular structure. GoogLeNet is known for its unique architecture and modular structure.
VGGNet (Visual Geometry Group Network) is a convolutional neural network (CNN) model developed in 2014 and described in “CNN Overview, Algorithms, and Examples of Implementations” that has achieved high performance in computer vision tasks. VGGNet was proposed by researchers in the Visual Geometry Group at the University of Oxford.
AlexNet is a deep learning model proposed in 2012 that represents a breakthrough in computer vision tasks. Convolutional Neural Networks (CNNs), which are primarily used for image recognition tasks.
The multi-class object detection model will be a machine learning model for performing the task of simultaneously detecting several objects of different classes (categories) in an image or video frame and enclosing the locations of these objects with bounding boxes. Multiclass object detection is used in important applications in computer vision and object recognition, and has been applied in various fields such as automated driving, surveillance, robotics, and medical image analysis.
Adding a head for refining position information (e.g., regression head) to the object detection model is a very important approach to improve the performance of object detection. This head helps to adjust the coordinates and size of the object bounding box to more accurately position the detected object.
Detecting small objects in image detection is generally a difficult task. Because small objects have few pixels, their features may be obscured and difficult to capture with normal resolution feature maps, making the use of image pyramids and high-resolution feature maps an effective approach in such cases.
- Deep learning with python and Keras What is deep learning?
Artificial intelligence is defined as “efforts to automate intellectual tasks that are normally performed by humans. This concept encompasses a number of approaches that have nothing to do with learning. Early chess programs, for example, simply incorporated rules hard-coded by programmers, and cannot be called machine learning.
For quite some time, many experts believed that in order to achieve a level of AI comparable to that of humans, a large enough number of rules to manipulate knowledge would have to be explicitly defined and manually incorporated by programmers. However, it was impossible to track down explicit rules for solving more complex and fuzzy problems like image classification, speech recognition, and language translation, and machine learning was born as a new approach to replace them.
A machine learning algorithm would be one where you give machine learning a sample of what you expect and it extracts rules to perform a data processing task. In machine learning and deep learning, the main task is “to transform data in a meaningful way. In other words, machine learning learns useful representations from given input data. These representations are then used to approach the expected output.
- Hello World of Neural Networks, Implementation of Handwriting Recognition with MNIST Data
As a hello world of deep learning technology, concrete implementation and evaluation of handwriting recognition technology for MNIST data by pyhton/Kera.
- Mathematical Elements in Neural Networks(1) Manipulating Tensors with numpy, etc.
In this article, we will discuss the manipulation of tensors, a mathematical element in neural networks, using numpy. In general, all current machine learning systems use tensors as the basic data structure. A tensor is essentially a container for data. In most cases, tensors are numerical data. Therefore, a tensor is a container for numerical data.
A tensor is defined by the following three main attributes. (1) Number of axes (factorial): for example, a 3D tensor has 3 axes and a matrix has 2 axes; in Python libraries such as Numpy, the number of axes is called the ndim attribute of the tensor; (2) Shape: an integer tuple representing the number of dimensions along each axis of the tensor; for example, in the example above, the shape of the matrix is (3 In the example above, for example, the shape of the matrix is (3,5), and the shape of the 3D tensor is (3,3,5). The shape of a vector is represented by a single element, such as (5,), while the shape of a scalar is empty ([]), (3) Data type: The type of data contained in the tensor, usually represented by dtype in Python libraries. For example, a tensor can be of type float32, uint8, or float64. It is important to note that most libraries, including Numpy, do not have tensors of type string. Note that most libraries, including Numpy, do not have tensors of type string, since strings are variable length and such an implementation is not possible.
- Mathematical elements in neural networks (2) Stochastic gradient descent method and error back propagation method
The stochastic gradient descent and error back propagation methods using tensors are described.
- Introduction to deep learning with python and Keras (1) Overview of how to use Keras
The specific Keras workflow (1) defining training data (input and objective tensors), (2) defining a network (model) consisting of multiple layers that map input values to objective values, (3) setting up the learning process by selecting a loss function, optimizer, and indicators to monitor, and (4) iteratively training the training data by calling the model’s fit method is described, and specific problems are solved.
- Introduction to deep learning with python and Keras (2) Practical application example (1) Two-class classification of text data
As an example of binary classification (two-class classification), the task of dividing a movie review into positive and negative reviews based on the content of the movie review text is described.
Collected from the IMDb (Internet Movie Database) set (preprocessed and included in Kelas), 50,000 “positive” or “negative” reviews with 50% negative and positive, respectively. Use 25,000 training data consisting of 50% of the reviews).
The actual calculation using the Dense and sigmaid functions using Keras is described.
- Introduction to Deep Learning with python and Keras (3) Practical Application Example (2) Multi-class Classification for News Delivery
We will build a network that classifies the reuters news feed data (packaged as part of Keras) into mutually exclusive topics (classes). Due to the large number of classes, this problem is an example of multiclass clasification. Each data point can be classified into only one category (topic). If you think about it, this is specifically a single-label multiclasss classification problem. If each data point can be classified into multiple categories (topics), then we are dealing with a multilabel multiclass classification problem.
We have implemented and evaluated this problem using Kera, mainly using the Dense layer and the Relu function.
- Introduction to Deep Learning with python and Keras (4) Practical Application Example (3) Regression for Predicting House Prices
We will discuss the application of regression to problems that predict continuous values rather than discrete labels (such as predicting tomorrow’s temperature based on weather data, or the time it will take to complete a project based on a software project specification).
The task is to predict the price of housing in the suburbs of Boston in the mid-1970s. For this prediction, we will use data points about the Boston suburbs at that time, such as crime rates and local property tax rates. The dataset contains a relatively small number of data points (506) and is divided into 404 training samples and 102 test samples. We also use different scales for the input data features (such as crime rate). For example, some show the rate as a value from 0 to 1, some take a value from 1 to 12, and some take a value from 0 to 100.
The approach is characterized by data normalization, using mean absolute error (MAE) and mean square error (MSE) as loss functions, and k-fold cross-validation to compensate for the small number of data.
We will discuss unsupervised learning. This category of machine learning finds important transformations of the input data without borrowing the value of the objective. Unsupervised learning may be aimed at data visualization, data compression, data denoising, or it may be aimed at gaining a better understanding of the correlations represented by the data. Unsupervised learning is an integral part of data analysis, and is often needed to gain a better understanding of a data set before solving supervised learning problems.
Two categories of unsupervised learning are well known: dimensionallity reduction and clustering. There are also self-learning methods such as autoencoder.
The paper also discusses over-learning and under-learning, and computational efficiency/optimization through regularization and dropout.
- Deep learning for computer vision with python and Keras (1) Convolution and pooling
In this article, we will discuss convolutional neural networks (CNNs), also known as cnvnet, a deep learning model that has been used almost without exception in computer vision applications. In this paper, we describe how to apply CNNs to the image classification problem of MNIST as handwritten character recognition.
- Deep learning for computer vision with python and Keras (2) Improving CNNs by Data Expansion with Small Amount of Data
We apply two more basic methods for applying deep learning to small data sets. One is feature extraction with pre-trained models, which improves the correctness rate from 90% to 96%. The second is fine tuning of the learned model, which will result in a final correctness rate of 97%. These three strategies (training a small model from scratch, feature extraction using the trained model, and fine tuning of the trained model) are some of the props that can be used when using a small dataset for attrition classification.
The dataset we will use is the Dogs vs Cats dataset, which is not packaged in Keras. This dataset will be the one provided by Kaggle’s Computer Vision Kompetition in late 2013. The original dataset can be downloaded from the Kaggle web page.
- Deep learning for computer vision with python and Keras (3) Improving CNNs using trained models.
In this article, we will discuss how to improve CNNs by using learned models. VGG16 is a simple CNN architecture widely used in ImageNet, which is a learned model consisting of classes representing animals and everyday objects. VGG16 is an older model, not quite up to the state of the art, and a bit heavier than many of the latest models.
There are two ways to use a trained network: feature extraction and fine-tuning.
- Deep learning for computer vision with python and Keras (4) Visualization of CNN training data
Since 2013, a wide range of methods have been developed to visualize and interpret these representations. In this article, we will focus on three of the most useful and easy-to-use methods.
(1) Visualization of the intermediate outputs of a CNN (activation of intermediate layers): This provides an understanding of how the input is transformed by the layers of the CNN and provides insight into the meaning of the individual filters of the CNN. (2) Visualization of CNN’s filters: To understand what kind of visual patterns and visual concepts are accepted by each filter of CNN. (3) Visualization of a heatmap of class activation in an image: This will allow us to understand which parts of an image belong to a particular class, and thus to localize objects in the image.
- DNN for text and sequence with python and Keras(1) Preprocessing text data for training
Deep Learning for Natural Language (Text) The two basic deep learning algorithms for processing sequences are recurrent neural networks (RNNs) and one-dimensional convolutional neural networks (CNNs).
The DNN model will be able to map the statistical structure of a sentence word at a level sufficient to solve many simple text processing tasks. Deep learning for Natural Language Processing (NLP) will be pattern recognition applied to words, sentences, and paragraphs in the same way that computer vision is pattern recognition applied to pixels.
Text vectorization can be done in multiple ways. (1) divide the text into words and convert each word into a vector, (2) divide the text into characters and convert each character into a vector, (3) extract the words or characters of an n-gram and convert the n-gram into a vector.
The vector can be in the form of one-hot encoding or word embedding. There are various learned word embedding databases available (Word2Vec, Global Vectors for Word Representation (GloVe), iMDb dataset).
- DNN for text and sequence with python and Keras(2)Applying SimpleRNN and LSTM
One of the common features of all coupled networks and convolutional neural networks will be that they do not have more memory. Each input passed to these networks is processed separately, and no state is maintained across these inputs. When processing sequences or time series data in such networks, the entire sequence needs to be provided to the network at once so that it can be treated as a single data point. Such a network is called a feedforward network.
In contrast, when people read a text, they follow the words with their eyes and memorize what they see. This allows the meaning of the sentence to be expressed in a fluid manner. Biological intelligence, while processing information in a novel way, maintains an internal model of what it is processing. This model is built from past information and is updated whenever new information is given.
Recurrent Neural Networks (RNNs) work on the same principle, though in a much simpler way. In this case, the processing of a sequence is done by iteratively processing the elements of the sequence. The information related to what is detected in the process is then maintained as state. In effect, an RNN is a kind of neural network with an inner loop.
In this paper, I describe the implementation of Simple RNN, which is a basic RNN using Keras, and LSTM and GRU, which are advanced RNNs.
- DNN for text and sequence with python and Keras(3)Advanced use of recurrent neural networks(GRU)
We describe an advanced method to improve the performance and generalization power of RNNs. In this paper, we take the problem of predicting temperature as an example, and access time-series data such as temperature, pressure, and humidity sent from sensors installed on the roof of a building. Using these data, we solve the difficult problem of predicting the temperature 24 hours after the last data point, and discuss the challenges we face when dealing with time series data.
Specifically, I describe an approach that uses recurrent dropout, recurrent layer stacking, and other techniques for optimization, and uses GRU (Gated Recurrent Unit) layers.
- DNN for text and sequence with python and Keras(4) Sequence processing with bidirectional RNNs and convolutional neural networks
The last method we will discuss is the bidirectional RNN (bidirectional RNN). Bidirectional RNNs are one of the most common RNNs and can perform better than regular RNNs in certain tasks. This RNN is often used in Natural Language Processing (NLP). As for bidirectional RNNs, they can be considered as versatile deep learning, like Swiss Army knives for NLP.
The feature of RNN is that it depends on the order (time). Therefore, shuffling the time increments or reversing the order may completely change the representation that the RNN extracts from the sequence. Bidirectional RNNs are built to capture patterns that are overlooked in one direction by processing sequences in the forward and reverse directions, taking advantage of the order-sensitive nature of RNNs.
- Advanced deep learning with python and Keras (1) Building complex networks with the Keras Functional API
In this article, we will discuss building a complex network model using the Keras Functional API as a best practice for more advanced deep learning.
When considering a deep learning model that predicts the market price of used clothing, the inputs to this model include user-provided metadata (such as the brand of the item and how old it is), user-provided text descriptions, and pictures of the item. The model is multimodal using these.
Some tasks may require prediction of multiple target attributes from the input data. A multi-output model for a tree where you have the text of a full-length novel or a short story, and you want to classify the novel by genre, but you also want to predict when the novel was written.
Or, for a combination of the above, you can use the Functional API in Keras to build a flexible model.
- Advanced deep learning with python and Keras (2) Model monitoring using Keras callbacks and TensorBord
In this article, I will discuss how to monitor what is happening in the model during training and optimization of DNN. When training a model, it is often difficult to predict from the beginning how many epochs are needed to optimize the loss value in the validation data.
For these epochs, if the training can be stopped when the improvement of the loss value in the validation data is no longer observed, the task can be performed more effectively. This is made possible by callbacks in Keras.
TensorBoard is a browser-based visualization tool that is included in TensoFlow. Note that TensorBoard can be used only when TensorFlow is used as a backend of Keras.
The main purpose of TensorBoard is to allow you to visually monitor everything that is happening inside the model during training, and if you are also monitoring information other than the final loss of the model, you will be able to see more clearly what the model is doing and not doing, and you will be able to quickly see the whole body. The capabilities of TesorBoead include (1) visual monitoring of metrics during training, (2) visualization of model architecture, (3) visualization of activation and gradient histograms, and (4) 3D exploration of embedding.
- Advanced deep learning with python and Keras (3) Model optimization methods.
In this article, I will discuss the optimization of models.
If all you need is something that works for the time being, you can blindly experiment with the architecture and it will work reasonably well. In this section, we will discuss an approach to make it work well enough to win a machine learning competition, instead of being satisfied with what works.
First, I will discuss “normalization” and “dw convolution” as important design patterns other than the residual connection mentioned above. These patterns become important when you are building a high-performance deep convolutional neural network (DCNN).
When building a deep learning model, you need to make a variety of decisions that seem to be at your personal discretion. Specifically, how many layers should there be in the stack? How many units or filters should be in each layer? What function should be used as the activation function? How many dropouts should be used? and so on. These architecture-level parameters are called hyperparameters to distinguish them from model parameters that are trained through back-propagation.
Another powerful method for obtaining the best results is model ensembling. An ensemble is a pooling of the predictions of different models to produce better predictions.
- Generative Deep Learning with python and Keras (1) Text generation using LSTM
In this article, we will discuss text generation using LSTM as generative deep learning with python and Keras.
As far as data generation using deep learning is concerned, in 2015, Google’s DecDream algorithm was proposed to transform images into psychedelic dog eyes and pared-down artworks, and in 2016, a short film called “sunspring” based on a script (with complete dialogues) generated by the LSTM algorithm, as well as the generation of various types of music.
These are achieved by using a deep learning model to extract samples from the statistical latent space of the learned images, music, and stories.
In this article, I will first describe a method for generating sequence data using a recurrent neural network (RNN). In this article, I will use text data as an example, but the exact same method can be applied to all kinds of sequence data (e.g., music, handwriting data in paintings, etc.). It can also be used for speech synthesis and dialogue generation in chatbots such as Google’s smart replay.
- Advanced Deep Learning with PyTorch(OpenPose, SSD, AnoGAN,Efficient GAN, DCGAN,Self-Attention, GAN, BERT, Transformer, GAN, PSPNet, 3DCNN, ECO)
Specific implementation and application of evolving deep learning techniques (OpenPose, SSD, AnoGAN, Efficient GAN, DCGAN, Self-Attention, GAN, BERT, Transformer, GAN, PSPNet, 3DCNN, ECO) using pyhtorch.
Reinforcement Learning
Reinforcement learning is a field of machine learning in which a learning system called an Agent learns optimal behavior through interaction with its environment. Unlike supervised learning, in which specific input data and output result pairs are provided, reinforcement learning is characterized by the provision of an evaluation signal called a reward signal.
This section provides an overview of reinforcement learning techniques and their various implementations.
Q-Learning (Q-Learning) is a type of reinforcement learning, which is an algorithm for agents to learn optimal behavior while exploring an unknown environment.Q-Learning provides a way for agents to learn an action value function (Q-function) and use this function to select optimal behavior.
The ε-greedy method (ε-greedy) is a simple and effective strategy for dealing with the trade-off between search and exploitation (exploitation and exploitation), such as reinforcement learning. The algorithm is a method to adjust the probability of choosing the optimal action and the probability of choosing a random action.
The Boltzmann distribution is one of the important probability distributions in statistical mechanics and physics, which describes how the states of a system are distributed in energy. The Boltzmann distribution is one of the probability distributions that play an important role in machine learning and optimization algorithms, especially in stochastic approaches and Monte Carlo based methods with a wide range of applications, such as The softmax algorithm can be regarded as a generalization of the aforementioned Boltzmann distribution, and the softmax algorithm can be applied to machine learning approaches where the Boltzmann distribution is applied as described above. The application of the softmax algorithm to the bandit problem is described in detail below.
A Markov Decision Process (MDP) is a mathematical framework in reinforcement learning that is used to model decision-making problems in environments where agents receive rewards associated with states and actions. and Markov properties of the process.
The algorithms integrating Markov decision processes (MDPs) described in “Overview of Markov decision processes (MDPs), algorithms and implementation examples” and reinforcement learning described in “Overview of reinforcement learning techniques and various implementations” are a combined approach of value-based and policy-based methods.
- Algorithms and implementation examples from the integration of inference and action using Bayesian networks
Integration of inference and action using Bayesian networks is a method in which agents use probabilistic models to select the most appropriate action while interacting with the environment, and Bayesian networks are a useful approach for representing dependencies between events and handling uncertainty. In this section, the Partially Observed Markov Decision Process (POMDP) is described as an example of an algorithm based on the integration of inference and action using Bayesian networks.
Thompson Sampling is an algorithm used in probabilistic decision-making problems such as reinforcement learning and multi-armed bandit problems, where the algorithm is used to select the optimal one among multiple alternatives (often called actions or arms) by It is designed to account for uncertainty. It will be particularly useful when the reward for each action is stochastically variable.
The Upper Confidence Bound (UCB) algorithm is an algorithm for optimal selection among different actions (or arms) in the Multi-Armed Bandit Problem (MBA), considering the uncertainty in the value of the actions, The method aims at selecting the optimal action by appropriately adjusting the trade-off between search and use.
SARSA (State-Action-Reward-State-Action) is a kind of control algorithm in reinforcement learning, which is mainly classified as a model-free method like Q learning. After observing the resulting reward \(r\), the agent learns a series of transitions until it selects the next action\(a’\) in a new state\(s’\).
Boltzmann Exploration is a method for balancing search and exploitation in reinforcement learning. Boltzmann Exploration calculates selection probabilities based on action values and uses them to select actions.
A2C (Advantage Actor-Critic) is an algorithm for reinforcement learning, a type of policy gradient method, which aims to improve the efficiency and stability of learning by simultaneously learning the policy (Actor) and value function (Critic).
Vanilla Q-Learning is a type of reinforcement learning, which is one of the algorithms used by agents to learn optimal behavior while interacting with their environment. Q-Learning is based on a mathematical model called the Markov Decision Process (MDP), in which the agent learns the value (Q-value) associated with a combination of State and Action, and selects the optimal action based on that Q-value.
C51, or Categorical DQN, is a deep reinforcement learning algorithm that models the value function as a continuous probability distribution. It has the ability to handle uncertainty by
Policy Gradient Methods are a type of reinforcement learning that focuses on policy optimization. A policy is a probabilistic strategy that defines what action an agent should choose for a state. Policy gradient methods aim to find the optimal strategy for maximizing reward by directly optimizing the policy.
Rainbow (“Rainbow: Combining Improvements in Deep Reinforcement Learning”) is a seminal work in the field of deep reinforcement learning that combines several reinforcement learning improvement techniques into an algorithm that improves the performance of DQN (Deep Q-Network) Rainbow outperformed other algorithms on many reinforcement learning tasks and has become one of the benchmark algorithms in subsequent research.
Prioritized Experience Replay (PER) is a technique for improving Deep Q-Networks (DQN), a type of reinforcement learning. ), and while it is common practice to randomly sample from the experience replay buffer, PER improves on this and becomes a way to preferentially learn important experiences.
Dueling DQN (Dueling Deep Q-Network) is an algorithm based on Q-learning in reinforcement learning and is a kind of value-based reinforcement learning algorithm. Dueling DQN is an architecture for efficiently estimating Q-values by learning state value functions and advantage functions separately, and this architecture was proposed as an advanced version of Deep Q-Network (DQN).
Deep Q-Network (DQN) is a combination of deep learning and Q-Learning, and is a reinforcement learning algorithm for problems with high-dimensional state spaces by approximating the Q-function with a neural network. Learning and uses techniques such as replay buffers and fixed target networks to improve learning stability.
Soft Actor-Critic (SAC) is a type of Reinforcement Learning algorithm that is primarily known as an effective approach for problems with continuous action spaces. Reinforcement Learning) and has several advantages over other algorithms such as Q-learning and Policy Gradients.
Proximal Policy Optimization (PPO) is a type of reinforcement learning algorithm and one of the policy optimization methods, which is based on the policy gradient method and designed for improved stability and high performance.
A3C (Asynchronous Advantage Actor-Critic) is a type of deep reinforcement learning algorithm that uses asynchronous learning to train reinforcement learning agents. A3C is particularly suited to tasks in continuous action spaces and has attracted attention for its ability to make effective use of large-scale computational resources.
Deep Deterministic Policy Gradient (DDPG) is an algorithm that extends the Policy Gradient method (Policy Gradient) in reinforcement learning tasks with continuous state space and continuous action space. deep neural networks to solve reinforcement learning problems in continuous action space.
- Overview of REINFORCE (Monte Carlo Policy Gradient) and Examples of Algorithms and Implementations
REINFORCE (or Monte Carlo Policy Gradient) is a type of reinforcement learning and a policy gradient method. REINFORCE is a method for directly learning policies and finding optimal action selection strategies.
- Actor-Critic Overview, Algorithm, and Implementation Examples
Actor-Critic is an approach to reinforcement learning that combines policy and value functions (value estimators).
- Overview of Trust Region Policy Optimization (TRPO) and Examples of Algorithms and Implementations
Trust Region Policy Optimization (TRPO) is a reinforcement learning algorithm, a type of Policy Gradient, that improves policy stability and convergence by optimizing policies under trust region constraints.
- Overview of Double Q-Learning and Examples of Algorithms and Implementations
Double Q-Learning is a type of Q-Learning described in “Overview of Q-Learning, Algorithms, and Examples of Implementations” and is one of the algorithms of reinforcement learning. It reduces the problem of overestimation and improves learning stability by using two Q functions to estimate Q values. This method has been proposed by Richard S. Sutton et al.
- Overview of Inverse Reinforcement Learning and Examples of Algorithms and Implementations
Inverse Reinforcement Learning (IRL) is a type of reinforcement learning in which the task is to learn the reward function behind the expert’s decisions from the expert’s behavioral data. Usually, in reinforcement learning, a reward function is given and the agent learns the policy that maximizes the reward function. Inverse Reinforcement Learning is the opposite approach, in which the agent analyzes the expert’s behavioral data and aims to learn the reward function corresponding to the expert’s decision making.
- Overview of Maximum Entropy Inverse Reinforcement Learning (MaxEnt IRL) and Examples of Algorithms and Implementations
Maximum Entropy Inverse Reinforcement Learning (MaxEnt IRL) is a method for estimating an agent’s reward function from expert behavior data. Typically, inverse reinforcement learning aims to observe how an expert behaves and find a reward function that can explain that behavior; MaxEnt IRL provides a more flexible and general approach by incorporating the Maximum Entropy principle in the estimation of the reward function. Entropy is a measure of the uncertainty of a probability distribution or prediction, and the maximum entropy principle is the idea of choosing the probability distribution with the highest uncertainty.
- Overview of Optimal Control-based Inverse Reinforcement Learning (OCIRL), Algorithm and Implementation Examples
Optimal Control-based Inverse Reinforcement Learning (OCIRL) is a method that attempts to estimate the reward function behind an agent’s behavior data when the agent performs a specific task. This approach is based on the theory of optimal control theory. This approach assumes that the agent acts based on optimal control theory.
- Overview of ACKTR and Examples of Algorithms and Implementations
ACKTR (Actor-Critic using Kronecker-factored Trust Region) is one of the algorithms of reinforcement learning, based on the idea of the Trust Region Method (Trust Region Policy Optimization, TRPO), It combines Policy Gradient Methods (Policy Gradient Methods) and value function learning, making it particularly suitable for control problems in continuous action spaces.
- Curly Window Search (Curiosity-Driven Exploration) Overview, Algorithm, and Implementation Examples
Curiosity-Driven Exploration is a general idea and method for improving learning efficiency in reinforcement learning by allowing agents to spontaneously find interesting states and events. This approach aims to allow the agent itself to self-generate information and learn based on it, rather than just a simple reward signal.
- Overview of the Value Gradient Method and Examples of Algorithms and Implementations
Value Gradients is a method used in the context of reinforcement learning and optimization that computes gradients based on value functions such as state values and action values, and uses these gradients to optimize measures.
- Machine Learning Startup Series “Reinforcement Learning in Python”
- Overview of Reinforcement Learning and Implementation of a Simple MDP Model
An overview of reinforcement learning and an implementation of a simple MDP model in python will be presented.
This section describes the method of planning based on the maze environment described in the previous section. Planning requires learning “value evaluation” and “strategy. To do this, it is first necessary to redefine “value” in a way that is consistent with the actual situation.
Here, we describe an approach using Dynamic Programming. This approach can be used when the transition function and reward function are clear, such as in a maze environment. This method of learning based on the transition function and reward function is called “model-based” learning. The “model” here refers to the environment, and the transition function and reward function that determine the behavior of the environment are the reality.
In this article, we will discuss the model-free method. Model-free is a method in which the agent accumulates experience by moving itself and learns from that experience. Unlike the model-based methods described above, it is assumed that information on the environment, i.e., transition function and reward function, is not known.
There are three points to be considered in utilizing the “experience” of the agent’s actions. (1) accumulation and balance of experience, (2) whether to revise plans based on actual results or forecasts, and (3) whether to use experience for value assessment or strategy update.
In this article, we discuss the trade-off between behavior modification based on actual performance and behavior modification based on prediction. We will discuss the Monte Carlo method for the former and the Temporal Difference Learning (TD) method for the latter. The Multi-step Learning method and the TD(λ) method (TD-Lambda method) are also described as methods that fall between the two.
In this article, I will discuss the difference between using experience for updating “value assessment” or “strategy”. This is the same as the difference between Value-based and Policy-based. We will look at the difference between the two, and also discuss a two-fold approach to updating both.
The major difference between value-based and policy-based learning is the criterion for action selection: value-based learning determines actions to move to the state with the greatest value, while policy-based learning determines actions based on strategy. The former criterion, which does not use strategy, is called Off-policy (no strategy = Off). In contrast, a school building that assumes a strategy is called On-policy.
Take Q-Learning as an example: the target of Q-Learning updates is “value evaluation,” and the criteria for action selection is Off-policy. This is evident from the fact that Q-Learning is implemented in such a way that it “takes action a to maximize value” (max(self.G[n-state])). In contrast, there is a method where the update target is “strategy” and the criterion is “on-policy”. That is SARSA (State-Action-Reward-State-Action).
In this article, we will discuss how to implement value functions and strategies with parameterized functions. This will allow us to deal with continuous states and actions that are difficult to handle in table management.
This time, we describe the implementation by pyhton in the framework of applying deep learning to reinforcement learning.
In this article, I will describe a method of replacing the value evaluation by a function with parameters, which is performed by a table (Q[s][a], Q-table) as described in “Implementation of model-free reinforcement learning in python (1) epsilon-Greedy method” etc. The function to perform value evaluation is called value function. The function that evaluates the value is called a value function, and learning (estimating) the value function is called Value Function Approximation (or simply Function Approximation). In value function-based methods, action selection is based on the output of the value function. In other words, it is a Value-based method.
In this article, we will create an agent that decides its action based on the value function and attack the CartPole environment, which is a popular environment in the OpenAI Gym and is used in various samples. A neural network is used for the value function.
In this article, we describe a game strategy using CNN. The basic mechanism is almost the same as the aforementioned, but the environment is changed in order to experience the advantage of direct screen input. This time, as a specific subject, we will describe Catcher, a game in which vol-catching is performed.
The Deep-Q-Network we have implemented here is currently undergoing many improvements, and Deep Mind, the company that introduced the Deep-Q-Network, has published a model called Rainbow that incorporates six excellent improvements (adding the Deep-Q-Network together makes a total of seven, or seven colors of Rainbow).
A strategy can also be represented by a function with parameters. It is a function that takes a state as an argument and outputs an action or action probability. However, it is not easy to update the parameters of the strategy. In value evaluation, there was a straightforward goal of bringing the estimated value closer to the actual value. However, the action or action probability output from the strategy cannot be directly compared to the value that can be calculated. The expected value of the value would be the learning tip in this case.
Just as we applied DNN to the value function, we can apply DNN to the strategy function. Specifically, it is a function that takes the game screen as input and outputs actions and action probabilities.
There were several variations of Policy Gradient, but here we describe a method called Actor Critic (A2C), which uses Advantage. The name “A2C” itself means only “Advantage Actor Critic,” but the method generally referred to as “A2C” includes methods that collect experience in a distributed environment in parallel. In this section, only the purely “A2C” part is implemented, and the distributed collection is only explained.
A3C (Asynchronous Advantage Actor Critic)” was published before A2C, and it uses the same distributed environment as A2C. The agent not only collects experience in each environment, but also learns. This is “asynchronous” learning (in each environment). However, A “2 “C was created because it was thought that sufficient or higher accuracy could be achieved without asynchronous learning, i.e., two “A “s were sufficient instead of three. Therefore, although it is not Asynchronous learning, the collection of experience in a distributed environment remains.
In “Applying Neural Networks to Reinforcement Learning: Applying Deep Learning to Strategies: Advanced Actor Critic (A2C),” it was mentioned that “Policy Gradient-based methods sometimes have unstable execution results,” and a method to improve this has been proposed. TRPO/PPO, along with the aforementioned A2C/A3C, are currently used as standard algorithms.
In the application of deep learning to reinforcement learning, “value evaluation” and “strategy” were each implemented as a function, and the function was optimized using neural networks. The correlation diagram of the main methods is shown below. There are three negative aspects of reinforcement learning as follows. (1) poor sample efficiency, (2) falling into locally optimal behavior, sometimes overlearning, and (3) poor reproducibility.
In this article, we will discuss methods to overcome the three weaknesses of reinforcement learning: “poor sample efficiency,” “falling into locally optimal behavior, often overlearning,” and “poor reproducibility. In particular, “poor sample efficiency” has become a major issue, and various countermeasures have been proposed. There are various approaches to these problems, but this time we will focus on “improvement of environment recognition.
In “Overview of Weaknesses of Deep Reinforcement Learning and Countermeasures and Two Approaches for Improving Environment Recognition,” I described methods for overcoming three weaknesses of deep reinforcement learning: “poor sample efficiency,” “falling into locally optimal behavior,” “often overlearning,” and “poor reproducibility. In particular, we focused on “improvement of environment recognition” as a countermeasure to the main issue of “poor sample efficiency. In this report, we describe the implementation of these methods.
- Overcoming Weaknesses in Deep Reinforcement Learning Dealing with Low Reproducibility: Evolutionary Strategies
Deep reinforcement learning has the problem of “unstable learning,” which has led to low reproducibility. Not only deep reinforcement learning, but also deep learning generally uses a learning method called the gradient method. Recently, evolutionary strategies (Evolution Startegies) have attracted attention as an alternative learning method to the gradient method. Evolutionary strategies are a classical method proposed at the same time as genetic algorithms and are very simple.
On a desktop PC (64-bit Corei-7 8GM), the above training can be done in less than one hour, which is much shorter than the usual reinforcement learning, and the reward can be obtained without a GPU. Optimization by evolutionary strategy is still under research, but it has the potential to rival the gradient method in the future. Research on the use or combination of other optimization algorithms to improve the reproducibility of reinforcement learning, rather than improving the gradient method, may be developed in the future.
- Overcoming Weaknesses in Deep Reinforcement Learning Dealing with Locally Optimal Behavior/Overlearning: Inverse Reinforcement Learning
Continuing from the previous article, this time we will discuss how to deal with locally optimal behavior and over-learning. Here, we discuss inverse reinforcement learning.
Inversed Reinforcement Learning (IRL) does not imitate the expert’s behavior but estimates the reward function behind the behavior. There are three advantages to estimating the reward function: first, it eliminates the need to design rewards, thereby preventing unintended behavior; second, it can be used for transfer to other tasks, and if the reward function is close, it can be used for learning another task (e.g., learning another game of the same genre); and third, it can be used for human learning. Third, it can be used to understand human (and animal) behavior.
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