Automatic generation by machine learning

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Automatic Generation by Machine Learning

Overview

Automatic generation through machine learning would be one in which the computer learns patterns and regularities in the data and generates new data based on them. There are several different approaches to automatic generation. Some of the most representative approaches are described below.

<Deep Learning Approach>

• Adversarial Generative Network (GAN): GAN described in “Overview of GANs and their various applications and implementations” is learned by pitting two networks, a generative model and a discriminative model. The generative model attempts to generate data similar to the training data, while the discriminative model is responsible for distinguishing them from the real data. The generative model learns to trick the discriminative model into generating more realistic data.
• Recurrent Neural Networks (RNN): RNN as described in “Overview of RNN and examples of algorithms and implementations” are used to generate serial data; RNNs can pass the previous state or output to the next step, making them suitable for generating data with temporal dependencies, such as generating sentences or music.
• Transformer, GPT: Both Transformer described in “Overview of Transformer Models, Algorithms, and Examples of Implementations“ and GPT described in “Overview of GPT and examples of algorithms and implementations” will be methods used for automatic generation of sequence data. These methods have been greatly developed in recent years.
• Variational Auto Encoder (VAE): VAE is used as a generative model; it learns a potential representation of the data and uses it to generate new data; VAE is built as a probabilistic model, which approximates the distribution of the data during training.

<Probabilistic Approach>

The probabilistic approach to generative modeling is a method that views the data generation process as a probabilistic model. This makes it possible to learn the characteristics and distribution of given data and generate new samples of data.

<Simulation-based Approach>

The Generative Model by Simulation approach uses machine learning and probabilistic models to generate events and processes within a computer model of the simulation. This allows real-world scenarios to be reproduced and applied for a variety of purposes.

Application Examples

Automatic generation by machine learning has a wide range of applications in various fields. Some typical applications are described below.

• Natural Language Processing (NLP)
  • Text generation: generating text such as articles, novels, poems, code, etc. Models such as GPT-3 can generate natural sentences based on context.
  • Chatbots: Chatbots that automatically generate user interaction. Used for customer support, providing information, etc.
• Image Generation
  • Image synthesis: combines elements of objects and landscapes to generate new images. For example, GANs (adversarial generative networks) are used to generate realistic-looking pictures.
  • Style transformation: Applying the style of one image to another to produce a new looking image. For example, the style of a famous painting may be applied to a photograph.
• Speech Processing
  • Text-to-speech: converts text into speech; used in AI assistants, navigation apps, etc.
  • Music generation: generates parts of or entire songs. May create new melodies or rhythms based on existing song styles.
• Medical
  • Medical image analysis: Detects lesions in medical images such as X-rays, MRIs, and CT scans to aid diagnosis. It is used for automatic detection of abnormal areas.
  • Disease prediction: Predicts the risk of disease based on a patient’s medical records and genetic information. It may be used to predict cancer and hereditary diseases.
  • Medical simulation: Models a patient’s medical condition, treatment efficacy, hospital operations, etc., for use in designing clinical trials and evaluating healthcare policies.
• Creative Industries
  • Character generation for movies and games: Automatic generation of character appearance and personality. Used in the production of movies and games.
  • Design assistance: Generates designs for logos, website designs, advertising banners, etc. Supports designers’ creativity.
  • Game development: In game development, probabilistic models are used to generate character behavior and environmental behavior to provide a realistic game experience.
• Finance
  • Stock price prediction: Analyzes past stock price data to predict future stock prices. Used to support investment decisions.
  • Fraud detection: Analyzes customer transaction history to detect fraudulent activity. It is useful in preventing credit card fraud.
• Weather Prediction.
  • Generates weather forecasts by modeling atmospheric behavior, ocean currents, and temperature fluctuations to simulate future weather conditions.

• Traffic Simulation
  • Model the behavior of road networks and public transportation systems to simulate traffic flow and congestion, and study measures to improve urban traffic.

• Ecosystem Modeling
  • Simulate the interactions of organisms within an ecosystem and changes in the environment to assess ecosystem stability and species survival.

Detailed technical topics for each approach are described below.

LLM, ChatGPT and Prompt Engineering

Fine Tuning of Large-scale Language Models and RLHF (Reinforcement Learning from Human Feedback)

Fine Tuning of Large-scale Language Models and RLHF (Reinforcement Learning from Human Feedback). Fine tuning of large-scale language models is the process of performing additional training on models that have been previously trained on a large data set, with the goal of enabling general-purpose models to be applied to specific tasks and domains to improve accuracy and performance.

Overview of LLM Fine Tuning with LoRA and Examples of Implementations

Overview of LLM Fine Tuning with LoRA and Examples of Implementations. LoRA (Low-Rank Adaptation) is a technique related to the fine tuning of large pre-trained models (LLMs), and was published in 2021 by Edward Hu et al. at Microsoft in their paper “LoRA: Low-Rank Adaptation of LoRA: Low-Rank Adaptation of Large Language Models” by Edward Hu et al.

Overview of Self-Refine and related algorithms and implementation examples

Overview of Self-Refine and related algorithms and implementation examples. Self-Refine consists of an iterative loop with two components, Feedback and Refine, which work together to produce high-quality output. Given the first output proposal generated by the model, it is iteratively refined over and over again, going back and forth between the two components Feedback and Refine. This process is repeated a specified number of times, or until the model itself decides that no further refinement is necessary.

Stable Diffusion and LoRA Applications

Stable Diffusion and LoRA Applications. Stable Diffusion is a method used in the field of machine learning and generative modeling, and is an extension of the Diffusion Models described in “Overview, Algorithms, and Examples of Implementations of Diffusion Models,” which are known generative models for images and audio. Diffusion Models are known for their high performance in image generation and restoration, and Stable Diffusion expands on this to enable higher quality and more stable generation.

Overview of ChatGPT and LangChain and its use

Overview of ChatGPT and LangChain and its use. LangChain is a library that helps develop applications using language models and provides a platform on which various applications using ChatGPT and other generative models can be built. One of the goals of LangChain is to enable it to handle tasks that language models cannot, such as answering questions about information outside the scope of knowledge learned by language models, or tasks that are logically complex or computationally demanding, etc. Another is to maintain it as a framework.

Agents and Tools in LangChain

Agents and Tools in LangChain. This section continues the discussion of LangChain, as described in “Overview of ChatGPT and LangChain and its use”. In the previous article, we described ChatGPT and LangChain, a framework for using ChatGPT and LangChain. This time, I would like to describe Agent, which has the ability to autonomously interfere with the outside world and transcend the limits of language models.

Overview of Prompt Engineering and its Use

Overview of Prompt Engineering and its Use. Prompt Engineering” refers to techniques and methods used in the development of natural language processing and machine learning models to devise a given text prompt (instruction) and elicit the best response for a particular task or purpose. This is a particularly useful approach when using large-scale language models such as OpenAI’s GPT (Generative Pre-trained Transformer). The basic idea behind prompt engineering is to obtain better results by providing appropriate questions or instructions to the model. The prompts serve as input to the model, and their selection and expression affect the output of the model.

Approach to automatic generation of prompts for generative AI

Approach to automatic generation of prompts for generative AI. Generative AI refers to artificial intelligence technologies that generate new content such as text, images, audio and video. As generative AI (e.g. image-generating AI and text-generating AI) generates new content based on given instructions (prompts), the quality and appropriateness of the prompts is key to maximising AI performance.

Controlling UI using generative AI

Controlling UI using generative AI. Controlling UI using generative AI is a useful technique for improving user interface design and interaction.

Problem solving by combining an issue analysis framework with generative AI

Problem solving by combining an issue analysis framework with generative AI. 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.

Metaverse control by natural language processing and generative AI

Metaverse control by natural language processing and generative AI. Metaverse manipulation by natural language will be a technology that allows users to intuitively control the objects, environment and avatar movements in the metaverse using natural language.

LIDAR (Light Detection and Ranging), generative AI and GNN

LIDAR (Light Detection and Ranging), generative AI and GNN. 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.

Abstraction-based approaches in summarisation and AI-based communication support

Abstraction-based approaches in summarisation and AI-based communication support. ‘Overview of automatic summarisation technology, algorithms and examples of implementation’ describes AI-based summarisation technology. Automatic summarisation technology is widely used in information retrieval, information processing, natural language processing, machine learning and other fields to compress large text documents and texts into short and to the point forms, and to facilitate the understanding of summarised information. It can be broadly divided into two types: extractive summarisation and abstractive summarisation. Here, we would like to consider a qualitative approach to abstractive summarisation based on the ‘one-word summarisation technique’.

Ontology Based Data Access (ODBA), generative AI and GNN

Ontology Based Data Access (ODBA), generative AI and GNN. Ontology Based Data Access (OBDA) is a method that allows queries to be performed on data stored in different formats and locations using a unified, conceptual view provided by an ontology, with the semantic integration of data and a user-friendly format for The aim will be to provide access to the data in a format that is easily understood by the user.

Overview of DeepPrompt and its use

Overview of DeepPrompt and its use. DeepPrompt is one of OpenAI’s programming support tools that uses natural language processing (NLP) models to support automatic code generation for programming questions and tasks DeepPrompt is a programming language syntax and semantics and can generate appropriate code when the user gives instructions in natural language.

Overview of the OpenAI Codex and its use

Overview of the OpenAI Codex and its use. OpenAI Codex is a natural language processing model for generating code from text, Codex will be based on the GPT series of models and trained on a large programming corpus Codex will understand the syntax and semantics and can generate appropriate programmes for tasks and questions given in natural language.

Overview of Automatic Sentence Generation with Huggingface

Overview of Automatic Sentence Generation with Huggingface. Huggingface is an open source platform and library for machine learning and natural language processing (NLP). The tools and resources provided by Huggingface are supported by an open source community, where there is an active effort to share code and models. This section describes the Huggingface Transformers, documentation generation, and implementation in python.

Codeless Generation Modules with text-generation-webui and AUTOMATIC1111

Codeless Generation Modules with text-generation-webui and AUTOMATIC1111. There are open source tools such as text-generation-webui and AUTOMATIC1111 that allow codeless use of generation modules such as ChatGPT and Stable Diffusion. In this article, we describe how to use these modules for text generation and image generation.

Overview of RAG (Retrieval-Augmented Generation) and Examples of its Implementation

Overview of RAG (Retrieval-Augmented Generation) and Examples of its Implementation. RAG (Retrieval-Augmented Generation) is one of the technologies attracting attention in the field of natural language processing (NLP), and is a method of constructing models with richer context by combining information retrieval (Retrieval) and generation (Generation). The main goal of RAG is to generate higher quality results by utilizing retrieved information in generative tasks (sentence generation, question answering, etc.). It is characterized by its ability to utilize knowledge and context.

Overview and example implementation of RAG using ChatGPT and LanChain

Overview and example implementation of RAG using ChatGPT and LanChain. The basic structure of RAG is to vectorize input queries with Query Encoder, find Documnet with similar vectors, and generate responses using those vectors. The vector DB is used to store the vectorized documents and to search for similar documents. Among these functions, as described in “Overview of ChatGPT and LangChain and their use”, ChatGPT’s API or LanChain is generally used for generative AI, and “Overview of Vector Database” is generally used for database. The database is generally described in “Overview of Vector Databases”. In this article, we describe a concrete implementation using these databases.

DPR and Hugging Face TransformerOverview and Implementation of a RAG Combining DPR and Hugging Face TransformerDPR

DPR and Hugging Face TransformerOverview and Implementation of a RAG Combining DPR and Hugging Face TransformerDPR. Dense Passage Retrieval (DPR) is one of the retrieval techniques used in the field of Natural Language Processing (NLP). DPR will be specifically designed to retrieve information from large sources and find the best answers to questions about those sources.

Overview of ReAct (Reasoning and Acting) and examples of its implementation

Overview of ReAct (Reasoning and Acting) and examples of its implementation. ReAct is one of the prompt engineering methods described in “Overview of prompt engineering and its use” and is also used in the scene of LangChain agent utilization described in “About Agents and Tools in LangChain “ReAct” is a “Reason”. ReAct” is a coined word from “Reasoning + Acting,” and the ReAct framework performs the following sequence processing.

Deep Learning Approach

Overview of pytorch, environment settings, and implementation examples

Overview of pytorch, environment settings, and implementation examples. PyTorch is a deep learning library developed by Facebook and provided as open source. It has features such as flexibility, dynamic computation graphs, and GPU acceleration, making it possible to implement a variety of machine learning tasks. Below we describe various examples of implementations using PyTorch.

Overview of the Seq2Seq (Sequence-to-Sequence) Model and Examples of Algorithms and Implementations

Overview of the Seq2Seq (Sequence-to-Sequence) Model and Examples of Algorithms and Implementations. The Seq2Seq (Sequence-to-Sequence) model is a deep learning model for taking sequence data as input and outputting sequence data, and in particular, it is an approach that can handle input and output sequences of different lengths. and dialogue systems, and is widely used in a variety of natural language processing tasks.

Overview of RNN and Examples of Algorithms and Implementations

Overview of RNN and Examples of Algorithms and Implementations. RNN (Recurrent Neural Network) is a type of neural network for modeling time-series and sequence data, and can retain past information and combine it with new information, such as speech recognition, natural language processing, video analysis, and time series prediction, It is a widely used approach for a variety of tasks.

Overview of LSTM and Examples of Algorithms and Implementations

Overview of LSTM and Examples of Algorithms and Implementations. LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN), which is a very effective deep learning model mainly for time series data and natural language processing (NLP) tasks. LSTM can retain historical information and model long-term dependencies, making it a suitable method for learning long-term information as well as short-term information.

Overview of Bidirectional LSTM and Examples of Algorithms and Implementations

Overview of Bidirectional LSTM and Examples of Algorithms and Implementations. Bidirectional LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) that is widely used for modeling sequence data such as time series data and natural language processing. Bidirectional LSTM is characterized by its ability to simultaneously learn sequence data from the past to the future direction and to capture the context of the sequence data more richly.

About GRU (Gated Recurrent Unit)

About GRU (Gated Recurrent Unit). GRU (Gated Recurrent Unit) described in “Overview of GRUs and examples of algorithms and implementations” is a type of recurrent neural network (RNN) that is widely used in deep learning models, especially for processing time series data and sequence data. The GRU is designed to model long-term dependencies in the same way as the LSTM (Long Short-Term Memory) described in “Overview of LSTM and Examples of Algorithms and Implementations,” but it is characterized by its lower computational cost than the LSTM. It is characterized by lower computational cost than LSTM.

About Bidirectional RNN (BRNN)

About Bidirectional RNN (BRNN). Bidirectional Recurrent Neural Network (BRNN) is a type of recurrent neural network (RNN) model that can simultaneously consider past and future information. BRNN is particularly useful for processing sequence data and is widely used in tasks such as natural language processing and It is widely used in tasks such as natural language processing and speech recognition.

About Deep RNN

About Deep RNN. Deep RNN (Deep Recurrent Neural Network) is a type of recurrent neural network (RNN), which is a stacked model of multiple RNN layers. deep RNN helps model complex relationships in sequence data and extract more sophisticated feature representations. Typically, a Deep RNN consists of RNN layers stacked in multiple layers in the temporal direction.

About Stacked RNN

About Stacked RNN. Stacked RNN (Stacked Recurrent Neural Network) is a type of recurrent neural network (RNN) architecture that uses multiple RNN layers stacked on top of each other, enabling modeling of more complex sequence data and effectively capturing long-term dependencies It is a method that allows for more complex sequence data modeling and the ability to effectively capture long-term dependencies.

Overview of Deep Graph Generative Models (DGMG), algorithms and implementation examples

Overview of Deep Graph Generative Models (DGMG), algorithms and implementation examples. 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.

Overview of GraphRNN, algorithms and implementation examples

Overview of GraphRNN, algorithms and implementation examples. 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.

About Echo State Network (ESN)

About Echo State Network (ESN). Echo State Network (ESN) is a type of reservoir computing, a type of recurrent neural network (RNN) used for prediction, analysis, and pattern recognition of time series and sequence data. tasks and may perform well in a variety of tasks.

Reservoir computing

Reservoir computing. Reservoir Computing (RC) is a type of recurrent neural network (RNN), which is a machine learning method that is particularly effective in processing time series data. The method simplifies the learning of complex dynamic patterns by keeping parts of the network (reservoirs) connected randomly.

Overview of Pointer-Generator Networks, Algorithms, and Examples of Implementations

Overview of Pointer-Generator Networks, Algorithms, and Examples of Implementations. The Pointer-Generator network is a type of deep learning model used in natural language processing (NLP) tasks, and is particularly suited for tasks such as abstract sentence generation, summarization, and information extraction from documents. The network is characterized by its ability to copy portions of text from the original document verbatim when generating sentences.

Overview of BERT and Examples of Algorithms and Implementations

Overview of BERT and Examples of Algorithms and Implementations. BERT (Bidirectional Encoder Representations from Transformers), BERT was presented by Google researchers in 2018 and is a deep neural network model pre-trained with a large text corpus and is one of the very successful pre-training models in the field of natural language processing (NLP). This section provides an overview of this BERT, its algorithms and examples of implementations.

Overview of GPT and Examples of Algorithms and Implementations

Overview of GPT and Examples of Algorithms and Implementations. GPT (Generative Pre-trained Transformer) is a pre-trained model for natural language processing developed by Open AI, based on the Transformer architecture and trained by unsupervised learning using large data sets. .

Overview of Conditional Generative Models and Implementation Examples

Overview of Conditional Generative Models and Implementation Examples. Conditional Generative Models are a type of generative model that has the ability to generate data given certain conditions. Conditional Generative Models play an important role in many application fields because they can generate data based on given conditions. This section describes various algorithms and concrete implementations of this conditional generative model.

ATTENTION in Deep Learning

ATTENTION in Deep Learning. Attention in deep learning is an important concept used as part of neural networks. The Attention mechanism refers to the ability of a model to assign different levels of importance to different parts of the input, and the application of this mechanism has recently been recognized as being particularly useful in tasks such as natural language processing and image recognition.

This paper provides an overview of the Attention mechanism without using mathematical formulas and an example of its implementation in pyhton.

Overview of BERT and Examples of Algorithms and Implementations

Overview of BERT and Examples of Algorithms and Implementations. BERT (Bidirectional Encoder Representations from Transformers), BERT was presented by Google researchers in 2018 and is a deep neural network model pre-trained with a large text corpus and is one of the very successful pre-training models in the field of natural language processing (NLP). The main features and overview of BERT are described below.

Overview of ULMFiT (Universal Language Model Fine-tuning), its algorithm, and examples of implementation

Overview of ULMFiT (Universal Language Model Fine-tuning), its algorithm, and examples of implementation. ULMFiT (Universal Language Model Fine-tuning) was proposed by Jeremy Howard and Sebastian Ruder in 2018 to effectively fine-tune pre-trained language models in natural language processing (NLP) tasks. It is an approach for fine tuning. The approach aims to achieve high performance on a variety of NLP tasks by combining transfer learning with fine tuning at each stage of training.

Overview of the Transformer Model and Examples of Algorithms and Implementations

Overview of the Transformer Model and Examples of Algorithms and Implementations. Transformer was proposed by Vaswani et al. in 2017 and will be one of the neural network architectures that have led to revolutionary advances in the field of machine learning and natural language processing (NLP). This section provides an overview of this Transformer model and its algorithm and implementation.

Overview of the Transformer XL and Examples of Algorithms and Implementations

Overview of the Transformer XL and Examples of Algorithms and Implementations. Transformer XL will be one of the extended versions of Transformer, a deep learning model that has proven successful in tasks such as natural language processing (NLP). Transformer XL is designed to more effectively model long-term dependencies in context and is able to process longer text sequences than previous Transformer models.

Overview of the Transformer-based Causal Language Model with Algorithms and Example Implementations

Overview of the Transformer-based Causal Language Model with Algorithms and Example Implementations. The Transformer-based Causal Language Model is a type of model that has been very successful in Natural Language Processing (NLP) tasks. The Transformer model (Transformer-based Causal Language Model) is a very successful model for natural language processing (NLP) tasks and is based on the Transformer architecture described in “Overview of the Transformer Model and Examples of Algorithms and Implementations. The following is an overview of the Transformer-based Causal Language Model.

Overview of Relative Positional Encoding and examples of algorithms and implementations

Overview of Relative Positional Encoding and examples of algorithms and implementations. Relative Positional Encoding (RPE) is a method for neural network models that use the transformer architecture to incorporate relative positional information of words and tokens into the model. Although transformers have been very successful in many tasks such as natural language processing and image recognition, they are not good at directly modeling the relative positional relationships between tokens. Therefore, RPE is used to provide relative location information to the model.

Overview of GANs and their various applications and implementations

Overview of GANs and their various applications and implementations. GAN (Generative Adversarial Network) is a machine learning architecture that is called a generative adversarial network. This model was proposed by Ian Goodfellow in 2014 and has since been used with great success in many applications. This section provides an overview of this GAN, its algorithms and various application implementations.

AnoGAN Overview, Algorithm and Implementation Example

AnoGAN Overview, Algorithm and Implementation Example. AnoGAN (Anomaly GAN) is a method that utilizes Generative Adversarial Network (GAN) for anomaly detection, especially applied to anomaly detection in medical imaging and quality inspection in the manufacturing industry. AnoGAN is an anomaly detection method that learns only normal data and uses it to find anomalies. Based on conventional GAN (Goodfellow et al., 2014), it trains the Generator (G) and Discriminator (D) to build a generative model that captures the characteristics of normal data.

Overview of Efficient GAN and Examples of Algorithms and Implementations

Overview of Efficient GAN and Examples of Algorithms and Implementations. Efficient GAN is a method to improve the problems of conventional Generative Adversarial Networks (GANs), such as high computational cost, learning instability, and mode collapse, especially in image generation, anomaly detection, and low-resource environments. It enables efficient learning and inference, especially in image generation, anomaly detection, and application in low-resource environments.

Self-Attention GAN Overview, Algorithm, and Implementation Examples

Self-Attention GAN Overview, Algorithm, and Implementation Examples. Self-Attention GAN (SAGAN) is a type of generative model, a form of Generative Adversarial Network (GAN) that introduces a Self-Attention mechanism to provide important techniques especially in image generation. It is specialized to model detailed local dependencies in the generated images.

DCGAN Overview, Algorithm and Example Implementation

DCGAN Overview, Algorithm and Example Implementation. DCGAN is a type of Generative Adversarial Network (GAN), a deep learning model specialized for image generation. DCGAN is a specialized modification of the GAN architecture.

SNGAN (Spectral Normalization GAN) Overview, Algorithms, and Examples of Implementations

SNGAN (Spectral Normalization GAN) Overview, Algorithms, and Examples of Implementations. SNGAN (Spectral Normalization GAN) is a method that introduces spectral normalization to stabilize the training of GAN (Generative Adversarial Network) as described in “Overview of GAN and Various Applications and Examples of Implementation”. This approach aims to suppress gradient explosion and disappearance and stabilize learning by applying spectral normalization to the weight matrix of the discriminator in particular.

Overview of BigGAN, Algorithm, and Example Implementation

Overview of BigGAN, Algorithm, and Example Implementation. BigGAN is a GAN (Generative Adversarial Network) proposed by researchers at Google DeepMind that is capable of generating high-resolution, high-quality images, especially when trained on large datasets (such as ImageNet) and when used in conjunction with the “Overview of GANs and Various Applications and Implementation Examples”, and by using a larger batch size than conventional GANs.

Overview of SkipGANomaly, Algorithm, and Example Implementations

Overview of SkipGANomaly, Algorithm, and Example Implementations. SkipGANomaly is a GAN-based method described in “Overview of GANs and Various Applications and Examples of Implementations” for the purpose of anomaly detection, which improves on conventional GANomaly by introducing skip connections. The GAN-based method, described in “Overview of GANs and Various Applications and Examples of Implementations,” improves on regular GANomaly by introducing skip connections.

Causal search using GAN (Generative Adversarial Network)

Causal search using GAN (Generative Adversarial Network). Causal search using GAN (Generative Adversarial Network) is a method of discovering causal relationships by utilising the opposing training processes of generative and discriminative models. The basic concepts and methods of causal search using GANs are presented below.

Overview of Variational Autoencoder Bayes (Variational Autoencoder, VAE) and Examples of Algorithms and Implementations

Overview of Variational Autoencoder Bayes (Variational Autoencoder, VAE) and Examples of Algorithms and Implementations. Variational Autoencoder (VAE) is a type of generative model and a neural network architecture for learning latent representations of data. The VAE learns latent representations by modeling the probability distribution of the data and sampling from it. An overview of VAE is given below.

Overview of Diffusion Models, Algorithms, and Examples of Implementations

Overview of Diffusion Models, Algorithms, and Examples of Implementations. 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.

Generative Deep Learning with python and Keras (1) Text generation using LSTM

Generative Deep Learning with python and Keras (1) Text generation using LSTM. In this article, we will discuss text generation using LSTM as generative deep learning with python and Keras.

As far as data generation using deep learning is concerned, in 2015, Google’s DecDream algorithm was proposed to transform images into psychedelic dog eyes and pared-down artworks, and in 2016, a short film called “sunspring” based on a script (with complete dialogues) generated by the LSTM algorithm, as well as the generation of various types of music.

These are achieved by using a deep learning model to extract samples from the statistical latent space of the learned images, music, and stories.

In this article, I will first describe a method for generating sequence data using a recurrent neural network (RNN). In this article, I will use text data as an example, but the exact same method can be applied to all kinds of sequence data (e.g., music, handwriting data in paintings, etc.). It can also be used for speech synthesis and dialogue generation in chatbots such as Google’s smart replay.

Advanced Deep Learning with PyTorch(OpenPose, SSD, AnoGAN,Efficient GAN, DCGAN,Self-Attention,  GAN, BERT, Transformer, GAN, PSPNet, 3DCNN, ECO)

Advanced Deep Learning with PyTorch(OpenPose, SSD, AnoGAN,Efficient GAN, DCGAN,Self-Attention,  GAN, BERT, Transformer, GAN, PSPNet, 3DCNN, ECO). Specific implementation and application of evolving deep learning techniques (OpenPose, SSD, AnoGAN, Efficient GAN, DCGAN, Self-Attention, GAN, BERT, Transformer, GAN, PSPNet, 3DCNN, ECO) using pyhtorch.

Combining 3D printers with generative AI and applying GNNs

Combining 3D printers with generative AI and applying GNNs. A 3D printer is a device for creating a three-dimensional object from a digital model, which is based on a computer-designed 3D model, which is then layered with materials to produce the object. This process is called additive manufacturing (additive manufacturing). The most common materials used are plastics, but metals, ceramics, resins, foodstuffs and even biomaterials are also used; the combination of GNNs, generative AI and 3D printers can enable complex structures and dynamic optimisation to create new design and manufacturing processes.

Probabilistic Approach

Probabilistic generative models can be a method to model the distribution of data and generate new data. In learning using a probabilistic generative model, a probability distribution such as Gaussian, Beta, or Dirichlet distribution as described in “Overview of Dirichlet distribution and related algorithms and implementation examples” is first assumed to model the distribution of the data, and then parameters to generate new data from the distribution are learned using algorithms such as variational and MCMC methods using methods such as maximum likelihood estimation described in “Overview of Maximum Likelihood Estimation and Algorithms and Their Implementations” and Bayesian estimation. The probabilistic generative model is supervised and learned.

Probabilistic generative models are used for unsupervised as well as supervised learning. In supervised learning, probabilistic generative models can be used to model the distribution of data and generate new data using the model. In unsupervised learning, the distribution of the data can be modeled to estimate latent variables. For example, in a clustering problem, data can be divided into multiple clusters.

Typical probabilistic generative models include topic models (LDA), hidden Markov models (HMM), Boltzmann machines (BM), autoencoders (AE), variational autoencoders (VAE), generative adversarial networks (GAN), etc.

These methods can be applied to natural language processing as represented by topic models, speech recognition using hidden Markov models, sensor analysis, and analysis of various statistical information including geographic information.

In the following pages of this blog, we discuss Bayesian modeling, topic modeling, and various applications and implementations using these probabilistic generative models.

Simulation Approach

Large-scale computer simulations have become an effective tool in a variety of fields, from astronomy, meteorology, physical properties, and biology to epidemics and urban growth, but only a limited number of simulations can be performed purely based on fundamental laws (first principles). Therefore, the power of data science is needed to set the parameter values and initial values that are the preconditions for the calculations. However, modern data science is even more deeply intertwined with simulation.

The following pages of this blog discuss these simulations, data science, and artificial intelligence.