Python and Machine Learning
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.
Machine Learning / Natural Language Processing/Image Recognition
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
Compassionate AI (Compassionate AI) and Empathetic AI (Empathetic AI) refer to AI that has emotional understanding and compassion and aims to respond with consideration for the emotional and psychological state of the user. These AIs can build a relationship of trust with the user through emotional recognition and natural conversation, and provide more personalised support, making them a technology of particular interest in fields where emotional support is required, such as healthcare, education, mental health and customer service work.
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.
Recursive Advantage Estimation will be a new approach that combines Markov decision processes (MDPs) and reinforcement learning. It is a methodology proposed by DeepMind in 2020.Recursive Advantage Estimation differs from regular reinforcement learning in that it uses measures and value functions with a recursive structure. The main idea of this approach would be to have recursivity in both the state transitions and rewards of the MDP. In a normal MDP, the next state and reward depend only on the previous state and action. However, Recursive Advantage Estimation uses past information more effectively by introducing recursive measures and value functions.
The Stable Marriage Problem (SMP) algorithm is a type of problem and solution method for achieving ‘stable matching’ between two groups. The most famous solution to this problem is the Gale-Shapley Algorithm (Gale-Shapley Algorithm), which can efficiently find stable combinations, and this algorithm has been used in particular for matching medical students and hospitals, job applicants and companies, and It will be applied to many real-world matching problems, in particular matching medical students with hospitals and job seekers with companies.
Surge pricing (dynamic pricing based on demand) will be one in which prices fluctuate under certain conditions and the optimum price is set in real time according to consumer demand and supply conditions. To achieve this, various machine learning and algorithms are utilised, with demand forecasting and market analysis techniques playing a particularly important role.
The Kelly criterion and equity-aware optimisation algorithms are methods used for various capital allocations. The Kelly criterion is a method for optimally allocating capital in gambling and investment, which calculates how much money should be invested when the expected value of an investment or bet is positive.
Predictive Control with Constraints (Predictive Control with Constraints) is a control method for designing control inputs to predict the future behaviour of a system while satisfying constraints. The method aims to optimise system performance under constraints.
- Overview, algorithms and implementation examples of linear quadratic programming (LQ problem)
Linear quadratic programming (LQ problem, Linear Quadratic Problem) is a widely used method in control theory and optimisation problems, and is particularly important in the field of optimal control.
- Linear Quadratic Control (LQC) overview, algorithms and implementation examples
Linear Quadratic Control (LQR) is a control theory and one of the optimal control methods for systems with linear dynamics.LQR is a method for obtaining feedback control laws to optimally control the state of a system, in particular to minimise the quadratic cost function, which is performance is used in designing control strategies that minimise the cost function based on the state and control inputs in order to optimise the performance of the system.
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.
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.
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).
SAM is one of the methods used in the context of causal inference, which aims to build models without relying on specific assumptions or prior knowledge when inferring causal relationships from data. Traditional causal inference methods typically use models based on specific causal structures and assumptions, but it is sometimes not clear whether these assumptions are accurate for real-world data. In addition, assumptions could lead to bias in causal estimation; SAM is a method for estimating causal effects without relying on such assumptions or prior knowledge, specifically, minimising causal assumptions and constraints when building models for estimating causal effects from data The emphasis will be on.
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.
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.
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.
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, algorithms and implementation examples of multi-step bootstrapping
Multi-step bootstrapping is a method used in the fields of statistics and machine learning, particularly for assessing uncertainty, and is an extension of the general bootstrapping method (a method for constructing a distribution of estimates by randomly re-extracting sample data) described in ‘Overview of ensemble learning, algorithms and implementation examples’. Extensions of the common bootstrap method (which constructs a distribution of estimates by re-extracting sample data at random) described in ‘Overview of ensemble learning and examples of algorithms and implementations’.
- Considerations for making agents behave intelligently
In this article, we consider how to make agents behave intelligently, as described in ‘Artificial life and agent technology’.
- Ensemble learning and multi-agent systems
Consider the application of multi-agent systems (MAS) to ensemble learning. Examples include distributed ensemble learning and its use in collaborative learning environments. These approaches combine the power of ensemble learning with the distributed processing and collaborative behaviour of multi-agent systems to improve overall system performance.
- On the implementation of ReAct by multi-agent systems
ReAct (Reasoning and Acting), described in ‘Overview of ReAct (Reasoning and Acting) and examples of its implementation’, is one of the architectures of AI systems, where agents solve problems and make decisions through a cycle of reasoning and action. It is a framework. In the implementation of ReAct in multi-agent systems, multiple agents work cooperatively or competitively, influencing each other to proceed with tasks.’ In a multi-agent system (MAS), which is also discussed in ‘Introduction to multi-agent systems’, multiple agents operate simultaneously and solve problems through cooperation or conflict The following implementation steps are possible when applying the ReAct architecture to a MAS.
By combining the issue analysis framework with ChatGPT, an AI-assisted problem-solving solution can be built. This solution makes it possible to gain efficient and deep insights by utilising the characteristics of generative AI in an existing framework. An example of a concrete solution procedure is given below.
Autonomous artificial intelligence technology can be defined as technology that has the ability to enable artificial intelligence to learn and solve problems on its own. In order to achieve this, functions such as self-learning, self-judgment, self-repair and self-replication are considered necessary.
Given that life was ‘inevitably’ or ‘accidentally’ created, the natural next step is to consider whether humans can do it. While advances in science and technology have improved our understanding of the origins of life and its reproducibility, the question of what it means to ‘fully create’ life is still being debated.
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.
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.
Few-Shot Learning is a method for correctly classifying and predicting new classes and tasks from a small number of training examples, and is mainly used in image recognition, natural language processing (NLP), speech recognition, and medical diagnosis. This approach is mainly used in applications where only limited data is available, such as image recognition, natural language processing (NLP), speech recognition, and medical diagnosis.
- Zero-Shot Learning Overview, Algorithm and Implementation Examples
Zero-Shot Learning (ZSL) is a method of classification and prediction for classes that have not been previously trained without additional training. This approach is characterized by its flexibility to work with unknown classes, whereas traditional machine learning and deep learning models can only accurately classify classes that have been learned.
- One-Shot Learning Overview, Algorithm, and Implementation Example
One-shot learning is a learning method that performs classification and recognition when only one training example exists for each class, and its goal is to achieve a model with high generalization performance even when data is scarce. The objective is to achieve a model with high generalization performance even when data is scarce. The method aims to effectively learn patterns from a limited data set and to have high discriminative power even for unknown classes.
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.
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.
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.
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.
- Modelling and the human imagination – modelling in philosophy, religion, literature and AI technology
In contrast to modelling in human endeavour, modelling with artificial intelligence (AI) technology aims to predict human behaviour, decision-making, knowledge, emotions and social interactions AI-based modelling is used to understand, optimise and improve complex systems and phenomena.
Structuralism is a theory of philosophy and social science based on the idea that human thought and culture are formed by “structure,” and was developed mainly in linguistics, philosophy, anthropology, and literary theory in the early to mid 20th century. Utilizing the perspectives of structuralism and meta-information is expected to provide deeper insights into the “invisible structure” behind information flows and data, and to provide hints for considering the future of digital society.
Spin is a concept used in physics, particularly quantum mechanics and solid state physics, defined as Consider combining the quantum concept of spin with AI algorithms. This would be a new approach to utilising the capabilities of quantum computers to extend the capabilities of AI technologies. The field is evolving through quantum machine learning and quantum information theory, and the use of quantum phenomena, especially spin, has the potential to improve the efficiency and accuracy of AI algorithms.
Quantum Neural Networks (QNN) are an attempt to utilise the capabilities of quantum computers to realise neural networks, as described in ‘Quantum Computers Accelerate Artificial Intelligence’, and exploit the properties of quantum mechanics to extend or improve conventional machine learning algorithms. It aims to extend or improve conventional machine learning algorithms by exploiting the properties of quantum mechanics.
Quantum Support Vector Machines (Q-SVMs) are an extension of the classical Support Vector Machines (SVMs) to quantum computing, as described in ‘Quantum Computing Overview and References/Reference Books’. SVMs are powerful algorithms for solving machine learning classification problems, and the power of quantum computing can be harnessed to improve their efficiency.
- Overview of the Max-Margin Approach and examples of algorithms and implementations
The Max-Margin Approach is a concept used in machine learning algorithms, in particular Support Vector Machines (SVMs), to determine the optimal boundary (hyperplane) in a classification problem, where the aim of the approach is to maximise the The margin between data points (the distance between the boundary and the nearest data point) is to be maximised.
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.
The Hidden Markov Model (HMM) described in “Overview of Hidden Markov Models, Various Applications and Implementation Examples” and the State Space Model (SSM) described in “Overview of State Space Models and Implementation Examples for Analysing Time Series Data Using R and Python” are statistical models used for modelling time-varying and serial data, but with different approaches. Model, SSM) are statistical models used for modelling temporal changes and series data, but with different approaches. The main differences between them are described below.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
Graph comparison analyses similarities and differences between data structures and is a method used in network analysis, bioinformatics, chemical structure analysis and machine learning. This enables a deeper understanding of structures and relationships, which can be useful for anomaly detection and feature extraction. The cost and Hungarian methods are described below in the graph comparison approach.
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.
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.
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.
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.
Metric Multidimensional Scaling (Metric MDS) is a method for embedding multidimensional data into a low-dimensional space and visualising similarities and distances between data, where given a distance (or similarity) between data, a point is It finds a low-dimensional space in which to place the points so as to represent them as faithfully as possible, given the distances (or similarities) between the data.
Non-metric MDS will be a method of embedding data into a low-dimensional space based on the similarity or dissimilarity of the data (often given as ‘ordinal data’ or ‘ranking’).’ Whereas metric MDS, as described in ‘Overview, algorithms and implementation examples of metric MDS’, seeks to preserve absolute values of distances (e.g. Euclidean distance), non-metric MDS prioritises the ‘ordinal relationship’ of the data.
- Self-Organising Map (SOM) overview, algorithms and implementation examples
Self-Organising Map (SOM) is a type of artificial neural network and a method for mapping and visualising high-dimensional data in a low-dimensional (usually two-dimensional) space. The maps are constructed in such a way that similar data are brought into close proximity, while preserving the features of the data. This method is useful for data clustering, dimensionality reduction and visualisation.
- Overview of Shepard’s method, algorithm and implementation examples
Shepard’s method is a non-linear dimensionality reduction method, specifically used as part of MDS, which is also described in ‘Multidimensional Scaling (MDS),’ and is mainly used to effectively map distances or similarities between data into a low-dimensional space. Shepard’s method can be characterised as a non-linear distance reduction method, an approach that is particularly capable of successfully representing diverse relationships between data.
- Overview of SMACOF (Scaling by Majorizing a Complex Function) and examples of algorithms and implementations
SMACOF is a type of MDS described in ‘Multidimensional Scaling (MDS)’ and is an algorithm for placing data in a low-dimensional space based on distance information. It is a particularly effective approach when dealing with non-linear data and approximate distance information.
ISOMAP (Isometric Mapping) is one of the non-linear dimensionality reduction methods and is an algorithm for embedding high-dimensional data into low-dimensional space. It is particularly effective when the data has a ‘manifold structure’, such as a curved distribution, and was proposed by Tenenbaum, De Silva, and Langford in 2000.
- Overview, algorithms and implementation examples of the Bias Correction Method in non-metric MDS
The Bias Correction Method in Non-Metric Multidimensional Scaling (NMS) is a technique for improving the accuracy of mapping from a distance matrix to a lower dimensional space, which is usually This method is usually used to deal with non-linearity and structural biases in the data that cannot be well represented by metric MDS, which is also described in ‘Overview of Metric MDS and Examples of Algorithms and Implementations’
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.
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.
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.
GNMT (Google Neural Machine Translation) is a neural machine translation system developed by Google that uses neural networks to provide natural translation between multiple languages.
OpenNMT (Open-Source Neural Machine Translation) is an open source platform for neural machine translation that supports translation model building, training, evaluation and deployment.
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 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.
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.
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).
In a ‘place’, a place or situation where people gather, people can share opinions and feelings and exchange values through communication, including physical places such as workplaces, schools and homes, places with a specific purpose or awareness such as meetings and events, or even virtual spaces such as the internet and the metaverse described in ‘History of the metaverse and challenges and AI support’, or even virtual spaces, such as the internet and the metaverse described in “History and challenges of the metaverse and AI support”. A ‘place’ is not just a space, but also a ‘situation’ or ‘opportunity’ for people, objects, ideas and energies that gather there to interact and create new values and meanings.
- Combining AI technologies and the metaverse
A metaverse is a virtual space or virtual world built on the internet. Here, users can interact with other people through avatars and engage in 3D spaces. Technologically, technologies such as virtual reality (VR), augmented reality (AR) and blockchain are often used. Combining AI technologies with the metaverse can significantly improve the convenience and user experience of virtual spaces.
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.
KBGAT (Knowledge-based Graph Attention Network) is a type of graph neural network (GNN) specialised for handling knowledge graphs (Knowledge Graph). KBGAT is based on the traditional Graph Attention Network (GAT) and is designed to take advantage of the special structure of knowledge graphs, with the following characteristics.
Deep Graph Infomax (DGI) is an unsupervised learning method for graph data, which is an information-theoretic targeted approach to learning node representations DGI aims to match local (node-level) and global (graph-level) features of a graph The aim is to obtain high-quality node embeddings by.
Edge-GNN (Edge Graph Neural Network) is a neural network architecture that focuses on edges in a graph structure and aims to process edge-level and graph-wide tasks by utilising edge features and weights. -GNNs differ from regular GNNs in that they focus primarily on edges rather than nodes (vertices). As information about the connections between nodes (edges) is important for many graph analysis tasks (e.g. relation prediction and link prediction), Edge-GNNs have the following properties.
Temporal Graph Neural Networks (TGNNs) are deep learning methods for processing graph structure data that take into account temporal information. TGNNs can model temporal changes of nodes, edges or entire graphs, and can take into account the effects of event order and time-dependence, and are suitable for handling dynamic graphs whose structure changes over time, e.g. It can handle, for example, changes in people’s relationships in a social network or changes in traffic in a communication network.
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.
Counterfactual learning with graph neural networks (GNNs) is a method for inferring outcomes under different conditions based on ‘what if’ assumptions for data with a graph structure. Counterfactual learning is closely related to causal inference and aims to understand the impact of specific interventions or changes on outcomes.
A metapath is a graph pattern for representing patterns between different edge types or node types in a heterogeneous graph, and in order to handle different edge types in a heterogeneous graph, it is necessary to properly define a metapath to represent each edge type. The following is a general procedure on how to define a metapath and handle different edge types in a non-homogeneous graph.
Metapath2Vec is one of the methods used for learning representations of nodes on graph data, where the method learns a dense vector representation of each node from a series of node data.Metapath2Vec is particularly suitable for heterogeneous graphs and Metapath2Vec is a useful approach especially when dealing with heterogeneous graphs and graph structures called metapaths.
LPA (Label Propagation Algorithm) is a type of graph-based semi-supervised learning algorithm, which aims at labelling unlabelled data through the propagation of labels from labelled to unlabelled nodes in a graph. LPA is also known as the label propagation method.
HARP (Hierarchical Attention Relational Graph Convolutional Network for Poverty Prediction) is a hierarchical attention relational graph convolutional network for poverty prediction. The method uses data with a graph structure (e.g. city demographics, geographical characteristics, socio-economic indicators, etc.) to predict poverty.
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) 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.
- Knowledge representation, machine learning, inference and GNN
Knowledge representation, as described in ‘Knowledge Information Processing Techniques’, and inference, as described in ‘Inference Techniques’, are important areas for structuring information and facilitating semantic understanding, whereas the application of Graph Neural Networks (GNNs), machine learning methods dedicated to processing graph-structured data, as described in The application of graph neural networks (GNNs) is one that allows for a more efficient and effective approach to the task of knowledge representation and inference.
Multi-agent systems using graph neural networks (GNNs) are a suitable approach when multiple agents interact in a graph structure and relationships and dependencies between agents are modelled.
Weather forecasting using graph neural networks (GNNs) is a novel approach to capture the complex spatial and temporal relationships in weather data. Traditional weather forecasting methods are dominated by numerical forecasting models (NWPs) and statistical methods, which are often computationally expensive and limited in their ability to improve overall accuracy; GNNs represent the relationships between data as graphs and utilise their structure to improve forecast accuracy, so their application to weather forecasting is Attention has been focused on.
Molecular simulation using graph neural networks is an approach that is expected to show higher accuracy and efficiency than conventional methods, and will be particularly notable for its ability to capture the complexity of molecular structures and interactions, and its ability to learn from large data sets.
Architectural structural design using graph neural networks (GNNs) is a method for automatically generating and evaluating building structures.
Urban intelligence is a technology and concept that collects and analyses data in cities and urban environments to improve urban management and services. Urban intelligence using graph neural networks (GNNs) captures the complex structures and relationships of cities by using them as graphs to models, which are then used to understand urban challenges and opportunities and to propose measures for improvement.
The service on modelling product properties and functions using Graph Neural Networks (GNN) and predicting market reactions and demand fluctuations is outlined below.
The service for designing new materials and predicting their properties using Graph Neural Networks (GNNs) will be aimed at increasing the efficiency of research and development in the field of materials science, reducing costs and rapidly discovering new high-performance materials. It uses GNNs to model the properties and structure of materials and has the capability to assist in the design of new materials and the prediction of their properties.
The service, which models each stage of the manufacturing process using Graph Neural Networks (GNN) and optimises the design and operation of the production line, is outlined as follows.
- Information Integration Theory and its applications
Information Integration Theory (IIT) is a theory proposed by psychologist Norman H. Anderson that is used to understand the process by which people integrate multiple pieces of information to make decisions and judgements It is a model. This model plays a particularly important role in cognitive and social psychology and represents how people’s judgements and evaluations are formed.
Consider combining Mathematical Logic with Graph Neural Network (GNN) technology as described in ‘Graph Neural Networks’ GNNs have the ability to process graph data consisting of nodes (objects) and edges (relations), making them a suitable approach for dealing with formal structures and rules in Mathematical Logic. This makes it a suitable approach for handling formal structures and rules in mathematical logic. This combination is expected to offer new possibilities in the areas of logical reasoning and knowledge representation.
LIDAR (Light Detection and Ranging, LIDAR) is a technology that uses laser light to measure the distance to an object and to accurately determine the 3D shape of the surrounding environment and objects. . This technology is used in a variety of fields, including automated driving, topographical surveying, archaeology and construction.
Power storage technology is a generic term for technologies that temporarily store power and release it when required, and is mainly used when supply and demand for power do not match, or to regulate the fluctuating generation of renewable energy sources. Combined, this power storage technology can be used to regulate energy fluctuations within a smart grid, as described below.
GNN is a deep learning technology for handling graph data, which learns the features of nodes and edges while considering directed/undirected relationships for graph structures represented by nodes (vertices) and edges (edges). This GNN technology is capable of capturing complex interdependencies between nodes and is being considered for application in various domains, making it a powerful machine learning method that can be applied to various aspects of semiconductor technology. In this article, specific applications of GNNs to semiconductor technology will be discussed.
IoT technology refers to physical devices being connected through the internet and exchanging data with each other, and IoT devices will comprise a wide variety of devices, such as sensors, actuators and cameras. Effectively analysing the vast amounts of data generated by these devices, and making predictions and optimisations, is a key challenge for IoT technology.GNNs are neural networks for processing data with a graph structure consisting of nodes and edges, and in an IoT environment, elements such as the following can be can be modelled.
Graphs are expressive and powerful data structures that are widely applicable due to their flexibility and effectiveness in modelling and representing graph-structured data, and are becoming increasingly popular in a variety of fields, including biology, finance, transport and social networks. Recommender systems are one of the most successful commercial applications of artificial intelligence, where user-item interactions can be naturally adapted to graph-structured data, and have attracted significant attention in the application of graph neural networks (GNNs). This section describes a recommender system based on GNNs.
Recommendation systems in Netflix
Recommendation systems in Netflix. The Netflix recommendation system will be based on information such as a user’s viewing history, ratings, search history, browsing time and favourites list, with the aim of suggesting the best content for that user. The system uses a combination of machine learning and algorithms. Specifically, the system can identify the user’s favourite genres, actors, directors, etc., based on their past viewing history, suggest content containing similar elements, and, by evaluating the films selected by the user, collect data to provide content tailored to the user’s preferred trends.
Lifted Relational Neural Networks (LRNN) is a type of neural network model designed to represent relational data and perform relational inference.
Deep Graph Generative Models (DGMG) is a type of deep learning model that specialises in graph generation tasks and is a particularly effective approach for generating complex graph structures.DGMG treats the graph generation process as a sequential decision problem and generates graph nodes and edges are generated in sequence.
GraphRNN is a deep learning model that specialises in graph generation and is particularly good at learning the structure of a graph and generating new graphs. The model generates entire graphs by predicting sequences of nodes and edges.
There are several methods for implementing multi-agent systems by deep reinforcement learning (DRL). The general methods are described below.
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.
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.
Heterogeneous Information Network Embedding (HIN2Vec) is a method for embedding heterogeneous information networks into a vector space, where a heterogeneous information network is a network consisting of several different types of nodes and links, for example HIN2Vec aims to effectively represent different types of nodes in a heterogeneous information network, and this technique is part of a field called Graph Embedding. It is part of a field called Graph Embedding, which aims to preserve the network structure and relationships between nodes by embedding them in a low-dimensional vector.
HIN2Vec-GAN is one of the techniques used to learn relations on graphs, specifically, it has been developed as a method for learning embeddings on Heterogeneous Information Networks (HINs) HINs are different graph structures with different types of nodes and edges, which are used to represent data with complex relationships.
HIN2Vec-PCA combines HIN2Vec and Principal Component Analysis (PCA) to extract features from Heterogeneous Information Networks (HINs).
R-GCN (Relational Graph Convolutional Network) is a type of neural network that performs convolutional operations on graph data. While normal graph convolutional operations deal with a single graph structure, R-GCN effectively performs convolutional operations on multiple graphs (heterogeneous information networks) that have different types of relations (relations). tasks. It will be developed for real-world data and problems where there are many nodes with different types of relations.
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 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.
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.
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.
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) 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.
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 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.
When considering solving real-world problems from probability, the perspectives of prediction and uncertainty are of paramount importance. This uncertainty and AI technology are closely related, and how AI handles decision-making in uncertain environments is an important topic in AI design and application. Uncertainty means that future outcomes are not entirely predictable, and how AI handles that uncertainty will influence different technological approaches.
KL divergence (Kullback-Leibler Divergence) is an asymmetric measure of similarity between probability distributions \(P \) and \(Q \), which is mainly used in information theory and machine learning. When treated as a constraint, it is mainly applied in optimisation problems and generative modelling.
Epistemic Uncertainty refers to uncertainty arising from a lack of, or incomplete, knowledge or information, and is caused by an inadequate understanding of an event or system, which can be reduced by acquiring more information or deepening existing knowledge This will be something that can be reduced by acquiring more information or deepening existing knowledge.
Aleatory Uncertainty refers to uncertainty that is mainly caused by natural phenomena and stochastic fluctuations. This type of uncertainty is inherently random and uncontrollable and is often expressed using probabilistic models. This applies, for example, to weather conditions or the roll of the dice.
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 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.
Forward Inference in Bayesian networks (Forward Inference) is a method for calculating the posterior distribution of variables and nodes in a network based on known information. Bayesian networks are probabilistic graphical models and are used to represent dependencies between variables. Forward Inference calculates the posterior distribution of the variable of interest through the propagation of information in the network.
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.
The Dirichlet Process (DP) is a powerful tool for dealing with infinite-dimensional probability distributions and plays a central role in Bayesian nonparametric models, which are applied to clustering and topic modeling.
The Hierarchical Dirichlet Process (HDP) is a Bayesian nonparametric method for handling infinite mixture models. The Bayesian nonparametric method is one of the Bayesian nonparametric methods for dealing with infinite mixture models.
- Chinese Restaurant Process Overview, Algorithm and Implementation Example
Chinese Restaurant Process Overview, Algorithm and Implementation Example. The Chinese Restaurant Process (CRP) is a probabilistic model used to intuitively explain the Dirichlet Process (DP), as described in “Overview of the Dirichlet Process (Dirichlet Process, DP), Algorithms, and Examples of Implementations. The Dirichlet Process (DP) is a probabilistic model used to intuitively explain the Dirichlet Process (DP). It is frequently used for clustering problems in particular.
The Stick-breaking Process is a typical method for intuitively understanding the Dirichlet Process (DP), as described in “Overview of the Dirichlet Process (DP), Algorithms, and Examples of Implementations. It is a typical approach to understand the Dirichlet Process (DP) intuitively, in which a bar of length 1 is infinitely and repeatedly divided at random to generate an infinite-dimensional probability distribution. This makes it a visually and mathematically beautiful way to construct discrete probability measures of Dirichlet processes.
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.
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.
Intersection over Union (IoU) is one of the evaluation metrics used in computer vision tasks such as object detection and region suggestion, and is an indicator of the overlap between the predicted bounding box and the true bounding box.
Anchor boxes in object detection is a concept widely used in convolutional neural network (CNN)-based object detection algorithms, where anchor boxes are used to represent candidate object regions at multiple locations and scales in an image.
Selective Search is one of the candidate region suggestion methods for object detection used in the field of computer vision and object detection, where object detection is the task of locating objects in an image, which is one of the key applications of computer vision. Selective Search helps object detection models to suggest regions where objects are likely to be present.
The EdgeBoxes algorithm is one of the candidate region suggestion methods for object detection. This method is used to locate potential objects in an image and efficiently and quickly suggests regions where objects are likely to be present.
Proposal networks are a type of neural network used mainly in the fields of computer vision and image processing, especially for object detection and region proposal (object proposal) tasks. A proposal network is a model for proposing a region of interest (an object or an area in which an object is present) from an input image.
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.
U-Net is one of the deep learning architectures in image segmentation (the task of assigning each pixel of an image to a corresponding class), proposed in 2015, this network is particularly useful in the field of medical image processing and semantic segmentation.
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.
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.
- Overview of WaveNet and examples of algorithms and implementations
WaveNet is a deep learning model for speech generation and will be a framework developed by DeepMind.WaveNet provides a neural network architecture for generating natural speech, the model uses convolutional neural networks (CNNs) to directly modelling speech waveforms on a sample-by-sample basis using.
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.
Strong AI (or Artificial General Intelligence, AGI: Artificial General Intelligence) refers to AI with broad intelligence that is not limited to a specific problem domain; AGI aims for AI that can learn and understand knowledge like humans and respond flexibly in different environments and situations, Beyond mere pattern recognition and optimisation, it needs to be capable of ‘intelligent reasoning’ and ‘deliberate action selection’. The ability to perform causal reasoning is said to be essential for a strong AI to have the same intellectual capacity as humans.
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 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.
Causal inference using Meta-Learners is one way to improve approaches to identifying and inferring causal relationships using machine learning models, where causal inference aims to determine whether one variable has a direct causal relationship to another variable, which can be done not only using traditional statistical methods As well as utilising machine learning, more sophisticated inference can be achieved using Meta-Learners, which are used to build models with the ability to rapidly adapt to different causal inference tasks, thereby enabling the efficient solution of
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.
The trace norm (or nuclear norm) is a type of matrix norm, which can be defined as the sum of the singular values of a matrix. It plays a particularly important role in matrix low-rank approximation and matrix minimisation problems.
The Frobenius norm is a type of matrix norm, defined as the square root of the sum of squares of the elements of a matrix. This means that the Frobenius norm of the matrix \( A \), \( ||A||_F \), is given by the following equation.
\[ ||A||_F = \sqrt{\sum_{i=1}^m \sum_{j=1}^n |a_{ij}|^2} \]
Where ὅ( A = [a_{ij}] \) is a \( m \times n \) matrix and the Frobenius norm corresponds to the Euclidean norm when the matrix is considered as a vector.
The atomic norm is a type of norm used in fields such as optimisation and signal processing, where the atomic norm is generally designed to reflect the structural properties of a vector or matrix.
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.
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.
The Finite Element Method (FEM) is a method for numerically analysing the behaviour and stress analysis of objects and structures, enabling detailed modelling of the effects of forces and loads on complex structures and objects and thereby calculating their physical behaviour, such as stress and displacement. The method is based on a finite volume method.
The Finite Volume Method (FVM) is a numerical solution method for solving partial differential equations, where the physical domain is divided into a finite number of cells and the behaviour of the entire domain is approximated by averaging and discretising the equations within each cell.
Sample-Based MPC (Sample-Based Model Predictive Control) is a type of model predictive control (MPC) that predicts the future behaviour of a system and calculates the optimum control input. It is a method characterised by its ease of application to non-linear and high-dimensional systems and its ease of ensuring real-time performance, compared with conventional MPC.
Real-Time Constraint Modification refers to technologies and methods for dynamically adjusting and modifying constraint conditions in real-time systems. Real-time systems are systems that require processing and response to take place within a specific time, typically used in embedded systems, control systems, communication systems, etc.
Physically Informed Neural Networks (PINNs) are a combination of data-driven machine learning approaches and physical modelling, using neural networks to model physical phenomena such as continuum mechanics and fluid dynamics, and using numerical solution methods to approximate the equations. The system will be able to approximate the equations.
- Overview of GraphNetworks used in physical simulation and examples of algorithms and implementations
The application of Graph Networks in physical simulation is a powerful method for modelling complex physical systems efficiently and accurately.
Graph Network-based Simulators (GNS) are powerful tools for physical simulation that use graph networks to predict the dynamic behaviour of physical systems, applicable to many physical systems with complex interactions.
Interaction Networks (INs) can be network architectures for modelling interactions between graph-structured data used in physical simulation and other scientific applications INs can model physical laws and data interactions.
- 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.
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