Python and Machine Learning (2) Deep Learning and Reinforcement Learning

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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.

深層学習

Post-training quantization（事後量子化）は、ニューラルネットワークの訓練が終了した後にモデルを量子化する手法であり、この手法では、通常の浮動小数点数で表現されているモデルの重みと活性化を、整数などの低ビット数で表現される形式に変換するものとなる。これにより、モデルのメモリ使用量が削減され、推論速度が向上する。以下に、Post-training quantizationの概要を示す。

  • FitNetによるモデルの蒸留の概要とアルゴリズム及び実装例について

FitNetは、モデルの蒸留（Distillation）手法の一つで、小規模な生徒モデルが大規模な教師モデルから知識を学習するための手法となる。FitNetは特に、異なるアーキテクチャを持つモデル同士の蒸留に焦点を当てている。以下に、FitNetによるモデルの蒸留の概要について述べる。

Quantization-Aware Training（QAT）は、ニューラルネットワークを効果的に量子化（Quantization）するための訓練手法の一つであり、量子化は、モデルの重みや活性化を浮動小数点数から整数などの低ビット数で表現するプロセスで、これによってモデルのメモリ使用量を削減し、推論速度を向上させることができるものとなる。Quantization-Aware Trainingは、この量子化を訓練中にモデルに組み込むことで、訓練中に量子化の影響を考慮したモデルを得る手法の一つとなる。

  • Attention Transferによるモデルの蒸留の概要とアルゴリズム及び実装例について

Attention Transferは、深層学習においてモデルの蒸留（Distillation）を行うための手法の一つであり、モデルの蒸留は、大規模で計算負荷の高いモデル（教師モデル）から小規模で軽量なモデル（生徒モデル）へ知識を転送するための手法となる。これにより、計算リソースやメモリの使用量を削減しつつ、生徒モデルが教師モデルと同様の性能を発揮できるようになる。

WordPieceは、自然言語処理（NLP）タスクで用いられるトークン化アルゴリズムの一つで、特に”BERTの概要とアルゴリズム及び実装例について“にも述べているBERT（Bidirectional Encoder Representations from Transformers）などのモデルで広く採用されている手法となる。

GloVe（Global Vectors for Word Representation）は、単語の分散表現（word embeddings）を学習するためのアルゴリズムの一種となる。単語の分散表現は、単語を数値ベクトルとして表現する方法であり、自然言語処理（NLP）のタスクで広く使用されている。GloVeは、特に単語の意味を捉えるために設計されており、単語の意味的な関連性をキャプチャする能力に優れている。ここでは、このGloveに関しての概要、アルゴリズム及び実装例について述べている。

FastTextは、Facebookが開発した自然言語処理（NLP）のためのオープンソースのライブラリで、単語埋め込み（Word Embeddings）の学習とテキスト分類などのNLPタスクの実行に使用できるツールとなる。ここではこのFastTextのアルゴリズム及び実装例について述べている。

Skip-gramは、単語の意味をベクトル表現として捉え、類似性や意味の関連性を数値化することが可能な自然言語処理（NLP）の分野で広く使われる単語の分散表現（Word Embedding）を学習するための手法の一つで、”DeepWalkの概要とアルゴリズム及び実装例について“で述べているDeepWalkなどのGNNでも用いられるものとなる。

ELMo（Embeddings from Language Models）は、自然言語処理（NLP）の分野で利用される単語埋め込み（Word Embeddings）の方法の一つであり、2018年に提案され、その後のNLPタスクで大きな成功を収めたものとなる。ここでは、このELMoの概要とアルゴリズム及び実装例について述べている。

BERT（Bidirectional Encoder Representations from Transformers）は、BERTは2018年にGoogleの研究者によって発表され、大規模なテキストコーパスを使って事前学習された深層ニューラルネットワークモデルであり、自然言語処理（NLP）の分野で非常に成功した事前学習モデルの一つとなる。ここでは、このBERTの概要とアルゴリズムおよび実装例について述べている。

GPT（Generative Pre-trained Transformer）は、オープンAIが開発した自然言語処理のための事前学習モデルであり、Transformerアーキテクチャを基にして、大規模なデータセットを使用して教師なし学習によって学習されるものとなる。

    ULMFiT（Universal Language Model Fine-tuning）は、2018年にJeremy HowardとSebastian Ruderによって提案された、自然言語処理（NLP）タスクにおいて事前学習済みの言語モデルを効果的にファインチューニングするためのアプローチとなる。このアプローチは、転移学習と訓練の段階ごとのファインチューニングを組み合わせて、さまざまなNLPタスクで高い性能を達成することを目的としている。

    Transformerは、2017年にVaswaniらによって提案され、機械学習と自然言語処理（NLP）の分野で革命的な進歩をもたらしたニューラルネットワークアーキテクチャの1つとなる。ここではこのTransformerモデルの概要とアルゴリズム及び実装について述べている。

    「トランスフォーマーXL」は、自然言語処理（NLP）などのタスクで成功を収めた深層学習モデルであるトランスフォーマー（Transformer）の拡張バージョンの一つとなる。トランスフォーマーXLは、文脈の長期依存関係をより効果的にモデル化することを目指して設計されており、過去のトランスフォーマーモデルよりも長いテキストシーケンスを処理できるようになっている。

    Transformer-based Causal Language Model（Transformativeベースの因果言語モデル）は、自然言語処理（NLP）タスクで非常に成功しているモデルの一種で、“Transformerモデルの概要とアルゴリズム及び実装例について“でも述べているTransformerアーキテクチャをベースにして、特に文章の生成タスクに適したものとなる。以下にTransformer-based Causal Language Modelの概要について述べる。

    Relative Positional Encoding（RPE）は、トランスフォーマー・アーキテクチャを使用するニューラルネットワークモデルにおいて、単語やトークンの相対的な位置情報をモデルに組み込むための手法となる。トランスフォーマーは、自然言語処理や画像認識などの多くのタスクで非常に成功しているが、トークン間の相対的な位置関係を直接モデリングするのは得意ではない。そのため、相対的な位置情報をモデルに提供するためにRPEが使用されている。

    GAN（Generative Adversarial Network）は、生成的敵対的ネットワークと呼ばれる機械学習のアーキテクチャとなる。このモデルは、2014年にイアン・グッドフェローによって提案され、その後多くの応用で大きな成功を収めている。ここでは、このGANの概要とアルゴリズムおよび様々な応用実装について述べている。

    AnoGAN (Anomaly GAN) は、異常検知のために Generative Adversarial Network (GAN) を活用する手法であり、特に、医療画像や製造業の品質検査などでの異常検知に適用されるものとなる。AnoGAN は、正常データのみを学習し、異常データの発見に利用する異常検知手法で、従来の GAN (Goodfellow et al., 2014) をベースに、Generator (G) と Discriminator (D) を訓練し、正常データの特徴を捉えた生成モデルを構築している。

    Efficient GAN は、従来の Generative Adversarial Networks (GANs) の課題である 計算コストの高さ、学習の不安定性、モード崩壊 (mode collapse) を改善するための手法で、特に 画像生成、異常検知、低リソース環境での適用 において効率的な学習と推論を可能にするものとなる。

    Self-Attention GAN (SAGAN) は、生成モデルの一種で、特に画像生成において重要な技術を提供するために、Self-Attention機構を導入したGenerative Adversarial Network（GAN）の一形態で、SAGANは、生成された画像の詳細な局所的な依存関係をモデル化することに特化したものとなっている。

    DCGANは、Generative Adversarial Network (GAN) の一種で、画像生成に特化した深層学習モデルとなっている。GANは、2つのネットワーク「生成器 (Generator)」と「識別器 (Discriminator)」を使って、生成モデルを学習させる方法だが、DCGANはそのGANのアーキテクチャに特化した改良を加えている。

    • PSPNet(Pyramid Scene Parsing Network)の概要とアルゴリズム及び実装例

    PSPNet（Pyramid Scene Parsing Network）は、シーン解析タスク、特にセマンティックセグメンテーションにおいて高い精度を実現するために提案されたディープラーニングモデルで、PSPNetは、視覚的な情報をより豊かに理解するために、複数の解像度でシーンを解析するというアイデアを採用している。これにより、局所的および広範な文脈情報を同時に取り入れることができ、精度の高いシーン解析を行うことが可能となる。

    • ECO(Efficient Convolution Network for Online Video Understanding)の概要とアルゴリズム及び実装例

    ECO（Efficient Convolutional Network for Online Video Understanding）は、オンライン動画理解のために設計された、効率的な畳み込みニューラルネットワーク（CNN）ベースのモデルであり、従来の3D CNNモデルの計算コストを削減しつつ、高い性能を維持するものとなる。

    • OpenPoseの概要とアルゴリズム及び実装例

    OpenPoseは、カーネギーメロン大学のペルソナ・コンピュータ・センター（Perceptual Computing Lab）によって開発された、リアルタイムで人間の姿勢を検出するライブラリで、人間の体、顔、手、足の位置を3Dまたは2Dで正確に推定することができるものとなる。この技術は、コンピュータビジョンやモーションキャプチャ、エンターテイメント、ヘルスケア、ロボティクスなど、さまざまな分野で広く使用されている。

    • SNGAN (Spectral Normalization GAN)の概要とアルゴリズム及び実装例

    SNGAN（Spectral Normalization GAN）は、”GANの概要と様々な応用および実装例について“で述べているGAN（Generative Adversarial Network）の訓練を安定化させるためにスペクトル正規化（Spectral Normalization）を導入した手法で、特に識別(Discriminator)の重み行列に対してスペクトル正規化を適用することで、勾配爆発や勾配消失を抑え、学習を安定化させることを目的としたアプローチとなる。

    • BigGANの概要とアルゴリズム及び実装例

    BigGANは、Google DeepMindの研究者によって提案された高解像度・高品質な画像生成が可能なGAN（Generative Adversarial Network）で、特に、大規模なデータセット（ImageNetなど）での学習と、”GANの概要と様々な応用および実装例について“で述べている従来のGANよりも大きなバッチサイズを利用することで、高精細な画像生成を実現したものとなる。

    • SkipGANomalyの概要とアルゴリズム及び実装例

    SkipGANomaly は、異常検知（Anomaly Detection）を目的とした “GANの概要と様々な応用および実装例について“で述べているGANベースの手法で、通常の GANomaly を改良し、スキップ接続（skip connections） を導入することで、異常検知の性能を向上させたものとなる。

    GAN (Generative Adversarial Network) を用いた因果探索は、生成モデルと識別モデルの対立する訓練プロセスを活用し、因果関係を発見する方法となる。以下に、GANを用いた因果探索の基本的な概念と手法を示す。

    深層学習のオープンソースフレームワークであるtensorflow、Kreasとpyhorchの比較を行なっている。

    ここではpython Kerasの概要と基本的な深層学習タスク(MINISTを用いた手書き文字認織、Autoencoder、CNN、RNN、LSTM)への具体的な適用例について述べている。

    Seq2Seq（Sequence-to-Sequence）モデルは、系列データを入力として取り、系列データを出力するための深層学習モデルであり、特に、異なる長さの入力系列と出力系列を扱うことができるアプローチとなる。Seq2Seqモデルは、機械翻訳、文章の要約、対話システムなど、さまざまな自然言語処理タスクで広く利用されている手法となる。

    RNN（Recurrent Neural Network）は、時系列データやシーケンスデータをモデル化するためのニューラルネットワークの一種であり、過去の情報を保持し、新しい情報と組み合わせることができるため、音声認識、自然言語処理、動画解析、時系列予測など、さまざまなタスクで広く使用されているアプローチとなる。

    LSTM（Long Short-Term Memory）は、再帰型ニューラルネットワーク（RNN）の一種であり、主に時系列データや自然言語処理（NLP）のタスクにおいて非常に効果的な深層学習モデルとなる。LSTMは、過去の情報を保持し、長期的な依存関係をモデル化することができるので、短期的な情報だけでなく、長期的な情報を学習するのに適した手法となる。

    Bidirectional LSTM（Long Short-Term Memory）は、リカレントニューラルネットワーク（RNN）の一種であり、時系列データや自然言語処理などのシーケンスデータに対するモデリングに広く使用されている手法となる。Bidirectional LSTMは、シーケンスデータを過去から未来方向へと同時に学習し、そのコンテキストをより豊かに捉えることができる特徴がある。

    GRU（Gated Recurrent Unit）は、”RNNの概要とアルゴリズム及び実装例について“でも述べている再帰型ニューラルネットワーク（RNN）の一種で、特に時系列データやシーケンスデータの処理に広く使用される深層学習モデルとなる。GRUは”LSTMの概要とアルゴリズム及び実装例について“で述べているLSTM（Long Short-Term Memory）と同様に長期的な依存関係をモデル化するために設計されているが、LSTMよりも計算コストが低いことが特徴となる。

    Bidirectional Recurrent Neural Network（BRNN）は、再帰型ニューラルネットワーク（RNN）の一種で、過去と未来の情報を同時に考慮することができるモデルとなる。BRNNは、特にシーケンスデータを処理する際に有用で、自然言語処理や音声認識などのタスクで広く使用されている。

    Deep RNN（Deep Recurrent Neural Network）は、再帰型ニューラルネットワーク（RNN）の一種で、複数のRNN層を積み重ねたモデルとなる。Deep RNNは、シーケンスデータの複雑な関係をモデル化し、より高度な特徴表現を抽出するのに役立ち、通常、Deep RNNは時間方向に多層で積み重ねられたRNNレイヤーから構成されている。

    Stacked RNN（スタックされた再帰型ニューラルネットワーク）は、再帰型ニューラルネットワーク（RNN）の一種で、複数のRNN層を積み重ねて使用するアーキテクチャであり、より複雑なシーケンスデータのモデリングが可能になり、長期依存性を効果的にキャプチャできるようになる手法となる。

    時空間ディープラーニング（Spatiotemporal Deep Learning）は、空間的および時間的なパターンを同時に学習するための機械学習技術であり、空間的な情報（位置や構造）と時間的な情報（時間的変化や遷移）を組み合わせて解析するため、特に時間と空間に関連する複雑なデータに対して効果的なアプローチとなる。

    ST-CNN（Spatio-Temporal Convolutional Neural Network）は、時空間データ（例えば、動画、センサーデータ、時系列画像など）を処理するために設計された畳み込みニューラルネットワーク（CNN）の一種で、従来のCNNを拡張して、空間的（Spatio）および時間的（Temporal）特徴を同時に学習することが目的の手法となる。

    3DCNN（3次元畳み込みニューラルネットワーク：3D Convolutional Neural Network）は、主に時空間データや3次元の特徴を持つデータを処理するための深層学習モデルの一種であり、画像データを扱う2DCNN（2次元畳み込みニューラルネットワーク）の拡張で、3次元空間における特徴抽出を行う点が特徴的な手法となる。

      リザーバーコンピューティング（Reservoir Computing、RC）は、リカレントニューラルネットワーク（RNN）の一種で、特に時系列データの処理において効果を発揮する機械学習手法となる。この手法は、ネットワークの一部（リザーバー）をランダムに接続された状態にしておくことで、複雑な動的パターンの学習を簡素化している。

      Echo State Network（ESN）は、リザーバーコンピューティングの一種で、時系列データやシーケンスデータの予測、分析、パターン認識などに使用されるリカレントニューラルネットワーク（RNN）の一種となる。ESNは、非常に効率的で簡単に訓練でき、さまざまなタスクで良好な性能を発揮することがある。

      Pointer-Generatorネットワークは、自然言語処理（NLP）のタスクで使用される深層学習モデルの一種であり、特に、抽象的な文章生成、要約、文書からの情報抽出などのタスクに適した手法となる。このネットワークは、文章を生成する際に、元の文書からのテキストの一部をそのままコピーすることができる点が特徴となる。

      Temporal Fusion Transformer (TFT) は、複雑な時系列データを扱うために開発されたディープラーニングモデルで、リッチな時間的依存関係をキャプチャし、柔軟な不確実性の定量化を可能にするための強力なフレームワークを提供するものとなる。

      CNN（Convolutional Neural Network）は、主に画像認識、パターン認識、画像生成などのコンピュータビジョンタスクに使用される深層学習モデルとなる。ここではこのCNNに関しての概要と実装例について述べている。

      DenseNet（Densely Connected Convolutional Network）は、2017年にGao Huang、Zhuang Liu、Kilian Q. Weinberger、およびLaurens van der Maatenによって提案された”CNNの概要とアルゴリズム及び実装例について“でも述べている深層畳み込みニューラルネットワーク（CNN）のアーキテクチャとなる。DenseNetは、畳み込みニューラルネットワークの訓練中に「密な（dense）」接続を導入することで、深層ネットワークの訓練の効率性を向上させ、勾配消失問題を軽減している。

      ResNetは、2015年にKaiming Heらによって提案された”CNNの概要とアルゴリズム及び実装例について“でも述べている深層畳み込みニューラルネットワーク（CNN）のアーキテクチャであり、ResNetは、非常に深いネットワークを効果的に訓練するための革新的なアイデアを導入し、コンピュータビジョンタスクにおいて驚異的な性能を達成したアプローチとなる。

      GoogLeNetは、Googleが2014年に発表した”CNNの概要とアルゴリズム及び実装例について“でも述べている畳み込みニューラルネットワーク（CNN）のアーキテクチャとなる。このモデルは、ImageNet Large Scale Visual Recognition Challenge（ILSVRC）などのコンピュータビジョンタスクにおいて、当時の最先端の性能を達成しており、GoogLeNetは、その特異なアーキテクチャとモジュール構造で知られいる。

      VGGNet（Visual Geometry Group Network）は、2014年に開発された”CNNの概要とアルゴリズム及び実装例について“でも述べている畳み込みニューラルネットワーク（CNN）のモデルで、コンピュータビジョンタスクにおいて高い性能を達成したものとなる。VGGNetは、University of OxfordのVisual Geometry Groupに所属する研究者によって提案されている。

      AlexNet（アレックスネット）は、2012年に提案されたディープラーニングモデルの一つであり、コンピュータビジョンタスクにおいて画期的な進歩をもたらした手法となる。AlexNetは、”CNNの概要とアルゴリズム及び実装例について“で述べている畳み込みニューラルネットワーク（Convolutional Neural Network、CNN）の一つで、主に画像認識タスクに使用される。

      人工知能の定義を「本来ならば人が行う知的な作業を自動化する取り組み」とする。この概念は学習とは無関係な多くのアプローチを含んでいる。例えば初期のチェスプログラムは、プログラマーによりハードコーディングされたルールを組み込んでいるだけで、機械学習とは呼べものではない。

      かなり長い間、多くの専門家は「人間に匹敵するレベルのAIを実現するには、知識を操作するのに十分な大量のルールを明示的に定義して、プログラマが手作業で組み込む必要がある」と考えていたが、画像分類、音声認識、言語の翻訳のように、より複雑でファジーな問題を解くための明示的なルールを突き止めるのは到底無理で、それらに変わる新しいアプローチとして、機械学習が生まれた。

      機械学習のアルゴリズムは、期待されるもののサンプルを機械学習に与えると、データ処理タスクを実行するためのルールが抽出されるものとなる。機械学習とディープラーニングでは「データを意味のある形で変換すること」が主な課題となる。つまり、機械学習は与えられた入力データから有益な表現(representation)を学習する。それらの表現は、期待される出力に近づくためのものとなる。

      深層学習技術のhello worldとして、pyhton/KeraによるMNISTデータの手書き認識技術の具体的な実装と評価

      今回は、ニューラルネットワークでの数学的要素であるテンソルのnumpyによる操作について述べる。一般に、現在の機械学習システムはすべて、基本的なデータ構造としてテンソルを使用する。テンソルは基本的には、データのコンテナ(入れ物)となる。ほとんどの場合、テンソルは数値データとなる。従ってテンソルは数値のコンテナとなる。

      テンソルは以下に示す3つの主な属性により定義される。(1)軸の数(階数):たとえば3次元テンソルの軸は3であり、行列の軸は2となる。NumpyなどのPythonライブラリでは、軸の数をテンソルのndim属性と呼ぶ、(2)形状:テンソルの各軸にそった次元の数を表す整数のタプル、例えば前述の例では、行列の形状は(3,5)であり、3次元テンソルの形状は(3,3,5)となる。ベクトルの形状は(5,)のように単一の要素で表されるが、スカラーの形状は空([])となる、(3)データ型:テンソルに含まれているデータの型。Pythonライブラリでは、通常はdtypeで表される。例えばテンソルの型はfloat32、uint8、float64などになる。まれにchar型のテンソルが使用されることもある。Numpyをはじめとするほとんどのライブラリでは、文字列型のテンソルは存在しないことに注意が必要となる。文字列は可変長であり、そのような実装は不可能だからである。

      テンソルを使った確率的勾配降下法と誤差逆伝播法について述べる。

      具体的なKerasのワークフロー(1)訓練データ(入力テンソルと目的テンソル)を定義する、(2)入力値を目的値にマッピングする複数の層からなるネットワーク(モデル)を定義する、(3)損失関数、オプティマイザ、監視する指標を選択することで、学習プロセスを設定する、(4)モデルのfitメソッドを呼び出すことで、訓練データを繰り返し学習する、について述べ、具体的な問題を解く。

      二値分類(2クラス分類)の例として、映画レビューのテキストの内容に基づいて、映画レビューを肯定的なレビューと否定的なレビューに分けるタスクについて述べる。

      IMDb(Internet Movie Database)データベースセット(前処理された状態でKerasに含まれている)から収集された、「肯定的」または「否定的な50,000件のレビューでそれぞれ否定的な50%のレビューと肯定的な50%のレビューで構成されている訓練用の25,000件のデータとテスト用の25,000件のデータを用いる。

      Kerasを用いてDense層とsigmaid関数を使って実際の計算を行ったものについて述べる。

      reutersのニュース配信データ(Kerasの一部としてパッケージされている)を相互排他なトピック(クラス)に分類するネットワークを構築する。クラスの数が多いため、この問題は多クラス問題(multiclass clasification)の一例となる。各データ点は一つのカテゴリ(トピック)にのみ分類される。そう考えると、これは具体的には、多クラス単一ラベル分類(single-label multiclasss classification)問題となる。各データ点が複数のカテゴリ(トピック)に分類される可能性がある場合は、多クラス多ラベル分類(multilabel multiclass classification)問題を扱うことになる。

      Keraを用いて主にDense層とRelu関数を用いて実装して評価している。

      離散的なラベルではなく連続値を予測する回帰(regression)問題への適用(気象データに基づいて明日の気温を予測したり、ソフトウェアプロジェクトの仕様に基づいてプロジェクトの完了にかかる時間を予測したりするmk)について述べる。

      1970年代中頃のボストン近郊での住宅価格を予測するタスクを行う。この予測には、犯罪発生率や地方財産税の税率など、当時のボストン近郊に関するデータ点を利用する。このデータセットに含まれるデータ点は506個と比較的少なく、404個の訓練サープルと、102個のテストサンプルに分割されている。また入力データの特徴量(犯罪発生率なと)はそれぞれ異なる尺度を利用している。例えば、割合を0〜1の値で示すものもあれば1〜12の値を取るものや、0〜100の値を取るものもある。

      アプローチの特徴としては、データ正規化を行い、損失関数として平均絶対誤差(mean absolute error、MAE)、二乗平均誤差(mean square error)を使い、k分割交差検証(k-fold cross-validation)を使うことでデータ数の少なさを補っている。

      教師なし学習について述べる。このカテゴリに分類される機械学習では、目的値の値を借りずに、入力データの重要な変換を見つけ出す。教師なし学習は、データの可視化、データの圧縮、データのノイズ除去が目的のこともあれば、データによって表される相関関係への理解を深めることが目的のこともある。教師なし学習は、データ解析に不可欠なものであり、教師あり学習の問題を解決する前にデータセットへの理解を深めるために必要になることもよくある。

      教師なし学習では、次元削減(dimensionallity reduction)とクラスタリング(clustering)の2つのカテゴリがよく知られている。さらにオートエンコーダー(autoencoder)のような自己学習もある。

      また過学習と学習不足、正則化やドロップアウトによる計算の効率化/最適化についても述べている。

      今回は畳み込みニューラルネットワーク(CNN)について述べる。CNNはコンピュータービジョンのアプリケーションにおいてほぼ例外なく使用されているディープラーニングモデルであり、cnvnetとも呼ばれる。ここでは、MNISTの手書き文字認識としての画像分類問題にCNNを適用する方法について述べる。

      小さなデータセットにディープラーニングを適用するための基本的な手法をさらに2つ適用する。一つは学習済みのモデルによる特徴抽出であり、これにより正解率が90%から96%に改善される。もう一つは、学習済みのモデルのファインチューニングであり、これにより、最終的な正解率は97%になる。これら3つの戦略(小さなモデルを1から訓練、学習済みモデルを使った特徴抽出、学習済みモデルのファインチューニング)は、小さなデータセットを使って無象分類するときの小道具の一つとなる。

      今回使用するデータセットは、Dogs vs Catsデータセットで、Kerasではパッケージ化されていない。このデータセットはKaggleの2013年後半のコンピュータービジョンこんぺで提供されたものになる。元のデータセットはKaggleのWebページからダウンロードされる。

      今回は学習済みモデルを利用したのCNNの改善について述べる。学習済みモデルとして2014年に、Karen SimonyanとAndrew Zissermanらによって開発されたVGG16アーキテクチャについて述べる。VGG16は、に動物や日常的なものを表すクラスで構成されている学習済みモデルであるImageNetで広く使用されているシンプルなCNNアーキテクチャとなる。VGG16は古いモデルで、最先端のモデルには遠く及ばず、最新の多くのモデルよりも少し重いものとなる。

      学習済みのネットワークを使用する方法には、特徴抽出(feature extraction)とファインチューニング(fine-tuning)の2つがある。

      CNNによって学習された表現は、それらが「視覚概念の表現」であるため、可視化に非常に適している。2013年以降、それらの表現を可視化/解釈するための手法は幅広く開発されている。今回はそれらの中で最も利用しやすく有益なものを3つ取り上げる。

      (1)CNNの中間出力(中間層の活性化)の可視化:CNNの一連の層によって入力がどのように変換されるかを理解し、CNNの個々のフィルタの意味を理解するものとなる。(2)CNNのフィルタの可視化:CNNの各フィルタが受け入れる視覚パターンや視覚概念がどのようなものであるかを把握できる。(3)画像におけるクラス活性化のヒートマップの可視化:画像のどの部分が特定のクラスに属しているかを理解でき、それにより、画像内のオブジェクトを局所化できるようになる。

      自然言語(テキスト)を扱う深層学習はシーケンスを処理するための基本的なディープラーニングアルゴリズムは、リカレントニューラルネットワーク(RNN)と一次元の畳み込みニューラルネットワーク(CNN)の2つとなる。

      DNNモデルで可能となるのは、多くの単純なテキスト処理タスクを解決するのに十分なレベルで、文語の統計的な構造をマッピングするものとなる。自然言語処理(Natural Language Processing:NLP)のためのディープラーニングは、コンピュータービジョンがピクセルに適用されるパターン認織であるのと同様に、単語、文章、段落に適用されるパターン認織となる。

      テキストのベクトル化は複数の方法で行うことができる。(1)テキストを単語に分割し、各単語をベクトルに変換する、(2)テキストを文字に分割し、各文字をベクトルに変換する、(3)Nグラムの単語または文字を抽出し、Nグラムをベクトルに変換する。

      ベクトルの形態としては、one-hotエンコード、単語埋め込み(word embedding)。学習済みの単語埋め込みのデーベースが様々に提供されている(Word2Vec、Global Vectors for Word Representation(GloVe)、iMDbデータセット)がある。

      全結合ネットワークや畳み込みニューラルネットワークなどに共通する特徴の一つは、記憶をもっといないこととなる。これらのネットワークに渡される入力はそれぞれ個別に処理され、それらの入力にまたがって状態が維持されることはない。そうしたネットワークでシーケンスや時系列データを処理するときには、シーケンス全体を一度にネットワークに提供することで、単一のデータ点として扱われるようにする必要がある。このようなネットワークはフィードフォワードネットワーク(feedforward network)と呼ばれる。

      これに対して、人が文章を読むときには、単語を目で追いながら、見たものを記憶していく。これにより、その文章の意味が流れるように表現される。生物知能は、情報を斬新的に処理しながら、処理しているものの内部モデルを維持する。このモデルは過去の情報から構築され、新しい情報が与えられるたびに更新される。

      リカレントニューラルネットワーク(RNN)も、非常に単純化されているものの、原理は同じとなる。この場合は、シーケンスの処理は、シーケンスの要素を反復的に処理するという方法で行われる。そして、その過程で検出されたものに関連する情報は、状態として維持される。実質的には、RNNは内部ループを持つニューラルネットワークの一種となる。

      ここではKerasを用いた基本的なRNNであるSimple RNNと、高度なRNNとしてLSTMの実装について述べている。

      RNNの性能と汎化力を向上させる高度な手法について述べる。ここでは気温を予測する問題を例に、建物の屋上に取り付けられたセンサーから送られてくる気温、気圧、湿度といった時系列データにアクセスする。それらのデータをもちに、最後のデータ点から24時間後の気温を予測するという難易度の高い問題を解き、時系列データを扱う時に直面する課題について述べる。

      具体的にはリカレントドロップアウト、リカレント層のスタック等のテクニックを用いて最適化し、GRU(Gated Recurrent Unit)層を利用するアプローチについて述べる。

      最後に述べる手法は、双方向RNN(bidirectional RNN)となる。双方向RNNは一般的なRNNの一つであり、特定のタスクにおいて通常のRNNよりもよい性能が得られる。このRNNは自然言語処理(NLP)でよく使用される。双方向RNNについては、NLPのためのスイスアーミイナイフのように万能なディープラーニングと考えられる。

      RNNの特徴は、順序(時間)に依存することとなる。そのため時間刻みをシャッフルしたり逆の順序にすると、RNNがシーケンスから抽出する表現がすっかり変わる可能性がある。双方向RNNは、RNNの順序に敏感な性質を利用して、順方向と逆の方向でのシーケンスを処理することで、一方向では見落としているパターンを捕捉することを目的として構築されたものとなる。

      今回はより高度なディープラーニングのベストプラクティスとしてKeras Functional APIを用いた複雑なネットワークモデルの構築について述べる。

      古着の市場価格を予測するディープラーニングモデルを考えた時、このモデルの入力は、ユーザーが提供するメタデータ(商品のブランドや何年まえのものかなど)、ユーザーが提供するテキストの説明、そして商品の写真などがある。これらを使ったマルチモーダルなモデル。

      タスクによっては、入力データから複数の目的属性を予測しなければならないことがある。長編小説や短編小説のテキストがあったとき、この小説をジャンル別に分類したいが、その小説がいつ頃執筆されたかも予測したいと木に必要なマルチ出力のモデル。

      あるいは上記を組み合わせたものに対して、KerasでのFunctional APIを用いることでフレキシブルなモデルを構築することができる。

      今回は訓練中にモデル内で起きていることを監視する手法とDNNの最適化について述べる。モデルを訓練する際には、検証データでの損失値を最適化するためにエポックが幾つ必要なのか等の最初から予測しておくことが困難なことが多々ある。

      このエポックに対して、検証データでの損失値の改善が認められなくなった時点で訓練を中止できれば、より効果的なタスクを行うことができる。これを可能にするのがKerasのコールバック(callback)となる。

      TensorBoardはTensoFlowに含まれているブラウザベースの可視化ツールとなる。なお、TensorBoardを利用できるのは、KerasのバックエンドとしてTensorFlowを使用している時に限られる。

      TensorBoardの主な目的は、訓練中にモデルの内部で起きていることをすべて視覚的に監視できるようにするとこで、モデルの最終的な損失以外の情報も監視している場合は、モデルが行なっていることと行なっていないことをより見通せるようになり、すばやく全身できるようになる。TesorBoeadの機能を以下のようになる(1)訓練中に指標を視覚的に監視、(2)モデルのアーキテクチャの可視化、(3)活性化と勾配のヒストグラムの可視化、(4)埋め込みを3次元で調査

      今回はモデルの最適化の手法について述べる。

      とりあえず動くものがあればそれで良いという場合には、アーキテクチャをやみくもに試してもそれなりにうまくいく。ここでは、うまくいくことに甘んじるのではなく、機械学習コンペに勝つほどうまくいくためのアプローチについて述べる。

      まず、前述した残差接続以外の重要な設計パターンとして「正規化」「dw畳み込み」について述べる。これらのパターンが重要になるのは、高性能なディープ畳み込みニューラルネットワーク(DCNN)を構築している場合となる。

      ディープラーニングモデルを構築する時には、個人の裁量にも思えるさまざまな決定を下す必要がある。具体的には、スタックの層の数はいくつにすればよいのか?各層のユニットやフィルタの数は幾つにすれば良いのか?活性化関数としてどのような関数を使うべきなのか?ドロップアウトはどれくらい使用すれば良いのか?等がある。こうしたアーキテクチャレベルのパラメータは、ばックプロパゲーション(誤差逆伝搬法)を通じて訓練されるモデルのパラメータと区別するために、ハイパーパラメータ(hyperparameter)と呼ばれる。

      最善の結果を得るためのもう一つの強力な手法は、モデルのアンサンブル(model ensembling)となる。アンサンブルは、よりよい予測値を生成するために、さまざまなモデルの予測値をプーリングする、というものになる。

      今回はpythonとKerasによるジェネレーティブディープラーニングとしてLSTMを用いたテキスト生成について述べる。

      深層学習を利用したデータの生成に関しては、2015年には、GoogleのDecDreamアルゴリズムによる画像をサイケデリックな犬の目やパレイドリックな作品に変換するもの、2016年にはLSTMアルゴリズムにより生成れさた(完全なセリフ付きの)脚本に基づいた「sunspring」という短編映画や、様々な音楽の生成が提案されている。

      これらは深層学習のモデルで、学習された画像、音楽、物語の統計的な潜在空間(latent space)から、サンプルを抽出することで実現されている。

      今回はまず、リカレントニューラルネットワーク(RNN)を使ってシーケンス(系列)データを生成する手法について述べる。ここではテキストデータを例に述べるが、全く同じ手法を使ってあらゆる種類のシーケンスデータ(音楽や絵画の筆跡データ等)への応用が可能となる。また、Googleのsmart replay等のチャットボットでの音声合成や対話生成にも活用することができる。

      pyhtorchを用いた発展的ディープラーニング技術の(OpenPose, SSD, AnoGAN,Efficient GAN, DCGAN,Self-Attention,  GAN, BERT, Transformer, GAN, PSPNet, 3DCNN, ECO)の具体的な実装と応用。

      Deep Learning

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

      Adversarial attack is one of the most widely used attacks against machine learning models, especially for input data such as images, text, and voice. Adversarial attacks aim to cause misrecognition of machine learning models by applying slight perturbations (noise or manipulations). Such attacks can reveal security vulnerabilities and help assess model robustness

      • 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.

      • Overview of Graph Network-based Simulators and examples of algorithms and implementations

      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.

      • Overview of Interaction Networks used in physical simulation and examples of related algorithms and implementations

      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.

      MeshGraphNets is a type of graph neural network (GNN) specialising in physical simulation and particularly good for simulations using mesh-based representations. MeshGraphNets represents mesh elements such as triangles and tetrahedra as graph nodes and edges, and enables physical simulation on them.

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

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

      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.

      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.

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

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

      Fine tuning of large-scale language models is the process of performing additional training on models that have been previously trained on a large data set, with the goal of enabling general-purpose models to be applied to specific tasks and domains to improve accuracy and performance.

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

      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.

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

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

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

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

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

      A comparison is made between tensorflow, Kreas and pyhorch, which are open source frameworks for deep learning.

      This section provides an overview of python Keras and examples of its application to basic deep learning tasks (handwriting recognition using MINIST, Autoencoder, CNN, RNN, LSTM).

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

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

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

      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.

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

      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.

      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.

      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.

      Spatiotemporal Deep Learning (Spatiotemporal Deep Learning) is a machine learning technique for learning spatial and temporal patterns simultaneously, combining spatial (position and structure) and temporal (temporal changes and transitions) information for analysis, making it a particularly It is an effective approach for complex data related to time and space in particular.

      ST-CNN (Spatio-Temporal Convolutional Neural Network) is a type of convolutional neural network (CNN) designed to process spatio-temporal data (e.g. video, sensor data, time-series images, etc.), extending traditional CNNs to The objective of the method is to learn spatial (Spatio) and temporal (Temporal) features simultaneously.

      3DCNN (3D Convolutional Neural Network) is a type of deep learning model for processing mainly spatio-temporal data and data with three-dimensional features, and is an extension of 2DCNN (2D Convolutional Neural Network), which is an extension of the 2DCNN (2-D Convolutional Neural Network), and is a distinctive method in that it performs feature extraction in 3-D space.

        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.

        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.

        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.

        The Temporal Fusion Transformer (TFT) is a deep learning model developed to handle complex time series data, which will provide a powerful framework for capturing rich temporal dependencies and enabling flexible uncertainty quantification.

        CNN (Convolutional Neural Network) is a deep learning model mainly used for computer vision tasks such as image recognition, pattern recognition, and image generation. This section provides an overview of CNNs and implementation examples.

        DenseNet (Densely Connected Convolutional Network) was proposed in 2017 by Gao Huang, Zhuang Liu, Kilian Q. Weinberger, and Laurens van der Maaten in “Overview of CNN DenseNet improves the efficiency of deep network training by introducing “dense” connections during convolutional neural network training, and reduces the gradient loss problem. and reducing the gradient loss problem.

        ResNet is a deep convolutional neural network (CNN) architecture proposed by Kaiming He et al. in 2015, as described in “CNN Overview, Algorithms and Implementation Examples”. ResNet introduces innovative ideas and approaches that have achieved phenomenal performance in computer vision tasks.

        GoogLeNet is a convolutional neural network (CNN) architecture described in Google’s 2014 “CNN Overview and Algorithms and Examples of Implementations”. This model achieved state-of-the-art performance in computer vision tasks such as the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), and GoogLeNet is known for its unique architecture and modular structure. GoogLeNet is known for its unique architecture and modular structure.

        VGGNet (Visual Geometry Group Network) is a convolutional neural network (CNN) model developed in 2014 and described in “CNN Overview, Algorithms, and Examples of Implementations” that has achieved high performance in computer vision tasks. VGGNet was proposed by researchers in the Visual Geometry Group at the University of Oxford.

        AlexNet is a deep learning model proposed in 2012 that represents a breakthrough in computer vision tasks. Convolutional Neural Networks (CNNs), which are primarily used for image recognition tasks.

        The multi-class object detection model will be a machine learning model for performing the task of simultaneously detecting several objects of different classes (categories) in an image or video frame and enclosing the locations of these objects with bounding boxes. Multiclass object detection is used in important applications in computer vision and object recognition, and has been applied in various fields such as automated driving, surveillance, robotics, and medical image analysis.

        The frame problem in agent systems refers to the difficulty for agents to properly understand the state and changes in the environment and to make decisions when acquiring new information. This is specifically the case in the following cases.

        Adding a head for refining position information (e.g., regression head) to the object detection model is a very important approach to improve the performance of object detection. This head helps to adjust the coordinates and size of the object bounding box to more accurately position the detected object.

        Detecting small objects in image detection is generally a difficult task. Because small objects have few pixels, their features may be obscured and difficult to capture with normal resolution feature maps, making the use of image pyramids and high-resolution feature maps an effective approach in such cases.

        Artificial intelligence is defined as “efforts to automate intellectual tasks that are normally performed by humans. This concept encompasses a number of approaches that have nothing to do with learning. Early chess programs, for example, simply incorporated rules hard-coded by programmers, and cannot be called machine learning.

        For quite some time, many experts believed that in order to achieve a level of AI comparable to that of humans, a large enough number of rules to manipulate knowledge would have to be explicitly defined and manually incorporated by programmers. However, it was impossible to track down explicit rules for solving more complex and fuzzy problems like image classification, speech recognition, and language translation, and machine learning was born as a new approach to replace them.

        A machine learning algorithm would be one where you give machine learning a sample of what you expect and it extracts rules to perform a data processing task. In machine learning and deep learning, the main task is “to transform data in a meaningful way. In other words, machine learning learns useful representations from given input data. These representations are then used to approach the expected output.

        As a hello world of deep learning technology, concrete implementation and evaluation of handwriting recognition technology for MNIST data by pyhton/Kera.

        In this article, we will discuss the manipulation of tensors, a mathematical element in neural networks, using numpy. In general, all current machine learning systems use tensors as the basic data structure. A tensor is essentially a container for data. In most cases, tensors are numerical data. Therefore, a tensor is a container for numerical data.

        A tensor is defined by the following three main attributes. (1) Number of axes (factorial): for example, a 3D tensor has 3 axes and a matrix has 2 axes; in Python libraries such as Numpy, the number of axes is called the ndim attribute of the tensor; (2) Shape: an integer tuple representing the number of dimensions along each axis of the tensor; for example, in the example above, the shape of the matrix is (3 In the example above, for example, the shape of the matrix is (3,5), and the shape of the 3D tensor is (3,3,5). The shape of a vector is represented by a single element, such as (5,), while the shape of a scalar is empty ([]), (3) Data type: The type of data contained in the tensor, usually represented by dtype in Python libraries. For example, a tensor can be of type float32, uint8, or float64. It is important to note that most libraries, including Numpy, do not have tensors of type string. Note that most libraries, including Numpy, do not have tensors of type string, since strings are variable length and such an implementation is not possible.

        The stochastic gradient descent and error back propagation methods using tensors are described.

        The specific Keras workflow (1) defining training data (input and objective tensors), (2) defining a network (model) consisting of multiple layers that map input values to objective values, (3) setting up the learning process by selecting a loss function, optimizer, and indicators to monitor, and (4) iteratively training the training data by calling the model’s fit method is described, and specific problems are solved.

        As an example of binary classification (two-class classification), the task of dividing a movie review into positive and negative reviews based on the content of the movie review text is described.

        Collected from the IMDb (Internet Movie Database) set (preprocessed and included in Kelas), 50,000 “positive” or “negative” reviews with 50% negative and positive, respectively. Use 25,000 training data consisting of 50% of the reviews).

        The actual calculation using the Dense and sigmaid functions using Keras is described.

        We will build a network that classifies the reuters news feed data (packaged as part of Keras) into mutually exclusive topics (classes). Due to the large number of classes, this problem is an example of multiclass clasification. Each data point can be classified into only one category (topic). If you think about it, this is specifically a single-label multiclasss classification problem. If each data point can be classified into multiple categories (topics), then we are dealing with a multilabel multiclass classification problem.

        We have implemented and evaluated this problem using Kera, mainly using the Dense layer and the Relu function.

        We will discuss the application of regression to problems that predict continuous values rather than discrete labels (such as predicting tomorrow’s temperature based on weather data, or the time it will take to complete a project based on a software project specification).

        The task is to predict the price of housing in the suburbs of Boston in the mid-1970s. For this prediction, we will use data points about the Boston suburbs at that time, such as crime rates and local property tax rates. The dataset contains a relatively small number of data points (506) and is divided into 404 training samples and 102 test samples. We also use different scales for the input data features (such as crime rate). For example, some show the rate as a value from 0 to 1, some take a value from 1 to 12, and some take a value from 0 to 100.

        The approach is characterized by data normalization, using mean absolute error (MAE) and mean square error (MSE) as loss functions, and k-fold cross-validation to compensate for the small number of data.

        We will discuss unsupervised learning. This category of machine learning finds important transformations of the input data without borrowing the value of the objective. Unsupervised learning may be aimed at data visualization, data compression, data denoising, or it may be aimed at gaining a better understanding of the correlations represented by the data. Unsupervised learning is an integral part of data analysis, and is often needed to gain a better understanding of a data set before solving supervised learning problems.

        Two categories of unsupervised learning are well known: dimensionallity reduction and clustering. There are also self-learning methods such as autoencoder.

        The paper also discusses over-learning and under-learning, and computational efficiency/optimization through regularization and dropout.

        In this article, we will discuss convolutional neural networks (CNNs), also known as cnvnet, a deep learning model that has been used almost without exception in computer vision applications. In this paper, we describe how to apply CNNs to the image classification problem of MNIST as handwritten character recognition.

        We apply two more basic methods for applying deep learning to small data sets. One is feature extraction with pre-trained models, which improves the correctness rate from 90% to 96%. The second is fine tuning of the learned model, which will result in a final correctness rate of 97%. These three strategies (training a small model from scratch, feature extraction using the trained model, and fine tuning of the trained model) are some of the props that can be used when using a small dataset for attrition classification.

        The dataset we will use is the Dogs vs Cats dataset, which is not packaged in Keras. This dataset will be the one provided by Kaggle’s Computer Vision Kompetition in late 2013. The original dataset can be downloaded from the Kaggle web page.

        In this article, we will discuss how to improve CNNs by using learned models. VGG16 is a simple CNN architecture widely used in ImageNet, which is a learned model consisting of classes representing animals and everyday objects. VGG16 is an older model, not quite up to the state of the art, and a bit heavier than many of the latest models.

        There are two ways to use a trained network: feature extraction and fine-tuning.

        Since 2013, a wide range of methods have been developed to visualize and interpret these representations. In this article, we will focus on three of the most useful and easy-to-use methods.

        (1) Visualization of the intermediate outputs of a CNN (activation of intermediate layers): This provides an understanding of how the input is transformed by the layers of the CNN and provides insight into the meaning of the individual filters of the CNN. (2) Visualization of CNN’s filters: To understand what kind of visual patterns and visual concepts are accepted by each filter of CNN. (3) Visualization of a heatmap of class activation in an image: This will allow us to understand which parts of an image belong to a particular class, and thus to localize objects in the image.

        Deep Learning for Natural Language (Text) The two basic deep learning algorithms for processing sequences are recurrent neural networks (RNNs) and one-dimensional convolutional neural networks (CNNs).

        The DNN model will be able to map the statistical structure of a sentence word at a level sufficient to solve many simple text processing tasks. Deep learning for Natural Language Processing (NLP) will be pattern recognition applied to words, sentences, and paragraphs in the same way that computer vision is pattern recognition applied to pixels.

        Text vectorization can be done in multiple ways. (1) divide the text into words and convert each word into a vector, (2) divide the text into characters and convert each character into a vector, (3) extract the words or characters of an n-gram and convert the n-gram into a vector.

        The vector can be in the form of one-hot encoding or word embedding. There are various learned word embedding databases available (Word2Vec, Global Vectors for Word Representation (GloVe), iMDb dataset).

        One of the common features of all coupled networks and convolutional neural networks will be that they do not have more memory. Each input passed to these networks is processed separately, and no state is maintained across these inputs. When processing sequences or time series data in such networks, the entire sequence needs to be provided to the network at once so that it can be treated as a single data point. Such a network is called a feedforward network.

        In contrast, when people read a text, they follow the words with their eyes and memorize what they see. This allows the meaning of the sentence to be expressed in a fluid manner. Biological intelligence, while processing information in a novel way, maintains an internal model of what it is processing. This model is built from past information and is updated whenever new information is given.

        Recurrent Neural Networks (RNNs) work on the same principle, though in a much simpler way. In this case, the processing of a sequence is done by iteratively processing the elements of the sequence. The information related to what is detected in the process is then maintained as state. In effect, an RNN is a kind of neural network with an inner loop.

        In this paper, I describe the implementation of Simple RNN, which is a basic RNN using Keras, and LSTM and GRU, which are advanced RNNs.

        We describe an advanced method to improve the performance and generalization power of RNNs. In this paper, we take the problem of predicting temperature as an example, and access time-series data such as temperature, pressure, and humidity sent from sensors installed on the roof of a building. Using these data, we solve the difficult problem of predicting the temperature 24 hours after the last data point, and discuss the challenges we face when dealing with time series data.

        Specifically, I describe an approach that uses recurrent dropout, recurrent layer stacking, and other techniques for optimization, and uses GRU (Gated Recurrent Unit) layers.

        The last method we will discuss is the bidirectional RNN (bidirectional RNN). Bidirectional RNNs are one of the most common RNNs and can perform better than regular RNNs in certain tasks. This RNN is often used in Natural Language Processing (NLP). As for bidirectional RNNs, they can be considered as versatile deep learning, like Swiss Army knives for NLP.

        The feature of RNN is that it depends on the order (time). Therefore, shuffling the time increments or reversing the order may completely change the representation that the RNN extracts from the sequence. Bidirectional RNNs are built to capture patterns that are overlooked in one direction by processing sequences in the forward and reverse directions, taking advantage of the order-sensitive nature of RNNs.

        In this article, we will discuss building a complex network model using the Keras Functional API as a best practice for more advanced deep learning.

        When considering a deep learning model that predicts the market price of used clothing, the inputs to this model include user-provided metadata (such as the brand of the item and how old it is), user-provided text descriptions, and pictures of the item. The model is multimodal using these.

        Some tasks may require prediction of multiple target attributes from the input data. A multi-output model for a tree where you have the text of a full-length novel or a short story, and you want to classify the novel by genre, but you also want to predict when the novel was written.

        Or, for a combination of the above, you can use the Functional API in Keras to build a flexible model.

        In this article, I will discuss how to monitor what is happening in the model during training and optimization of DNN. When training a model, it is often difficult to predict from the beginning how many epochs are needed to optimize the loss value in the validation data.

        For these epochs, if the training can be stopped when the improvement of the loss value in the validation data is no longer observed, the task can be performed more effectively. This is made possible by callbacks in Keras.

        TensorBoard is a browser-based visualization tool that is included in TensoFlow. Note that TensorBoard can be used only when TensorFlow is used as a backend of Keras.

        The main purpose of TensorBoard is to allow you to visually monitor everything that is happening inside the model during training, and if you are also monitoring information other than the final loss of the model, you will be able to see more clearly what the model is doing and not doing, and you will be able to quickly see the whole body. The capabilities of TesorBoead include (1) visual monitoring of metrics during training, (2) visualization of model architecture, (3) visualization of activation and gradient histograms, and (4) 3D exploration of embedding.

        In this article, I will discuss the optimization of models.

        If all you need is something that works for the time being, you can blindly experiment with the architecture and it will work reasonably well. In this section, we will discuss an approach to make it work well enough to win a machine learning competition, instead of being satisfied with what works.

        First, I will discuss “normalization” and “dw convolution” as important design patterns other than the residual connection mentioned above. These patterns become important when you are building a high-performance deep convolutional neural network (DCNN).

        When building a deep learning model, you need to make a variety of decisions that seem to be at your personal discretion. Specifically, how many layers should there be in the stack? How many units or filters should be in each layer? What function should be used as the activation function? How many dropouts should be used? and so on. These architecture-level parameters are called hyperparameters to distinguish them from model parameters that are trained through back-propagation.

        Another powerful method for obtaining the best results is model ensembling. An ensemble is a pooling of the predictions of different models to produce better predictions.

        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.

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

        Reinforcement Learning

        Reinforcement learning is a field of machine learning in which a learning system called an Agent learns optimal behavior through interaction with its environment. Unlike supervised learning, in which specific input data and output result pairs are provided, reinforcement learning is characterized by the provision of an evaluation signal called a reward signal.

        This section provides an overview of reinforcement learning techniques and their various implementations.

        Temporal Difference Error (TD Error) is a concept used in reinforcement learning that plays an important role in the updating of state value functions and behaviour value functions. TD errors are defined by using the Bellman equation to relate the value of one state or behaviour to the value of the next state or behaviour.

        Temporal Difference (TD) learning is a type of Reinforcement Learning, which is a method for agents to learn how to maximise rewards while interacting with their environment. TD learning uses the difference between the actual observed reward and the predicted future value (Temporal Difference) to update the prediction of future rewards.

        Feature-based Inverse Reinforcement Learning is a type of reinforcement learning and is a method for estimating the reward function of the environment from the expert’s behaviour. While regular Inverse Reinforcement Learning (IRL) directly learns the expert’s trajectory and estimates the reward function based on it, Feature-based Inverse Reinforcement Learning focuses on using features to estimate the reward function.

        Drift-based Inverse Reinforcement Learning (Drift-based Inverse Reinforcement Learning) is a method for detecting differences between the expert’s behaviour and the agent’s behaviour and estimating the reward function that minimises those differences. In ordinary inverse reinforcement learning (IRL), the expert’s behaviour is learned directly and the reward function is estimated based on it, and if the expert’s behaviour and the agent’s behaviour differ, it becomes difficult to estimate the reward function accurately, whereas in drift detection-based inverse reinforcement learning, the expert and The difference in the agent’s behaviour (drift) shall be detected and the reward function shall be estimated such that the drift is minimised.

        Q-Learning (Q-Learning) is a type of reinforcement learning, which is an algorithm for agents to learn optimal behavior while exploring an unknown environment.Q-Learning provides a way for agents to learn an action value function (Q-function) and use this function to select optimal behavior.

        The Policy Gradient Method is one of the methods in Reinforcement Learning (RL) in which an agent directly learns a policy (a policy for action selection), and this method uses a probabilistic function of the policy to select actions, By optimising the parameters of that function, it attempts to maximise the agent’s long-term reward.

        Advantage Learning is an enhanced version of Q-Learning and the Policy Gradient Method described in ‘Overview of Q-Learning, Algorithms and Implementation Examples’, and is a method for learning the difference between state values and behaviour values, or ‘advantage’. In conventional Q learning, the expected reward value (Q-value) obtained for a state-action pair is learned directly, whereas in advantage learning, an advantage function \(A(s,a)\) is calculated to evaluate how good the choice is relative to it.

        Generalised Advantage Estimation (GAE) is one of the methods used for policy optimisation in reinforcement learning, especially for algorithms that utilise state value functions or action value functions, such as the Actor-Critic approach. GAE adjusts the trade-off between bias and variance to achieve more efficient policy updating.

        The ε-greedy method (ε-greedy) is a simple and effective strategy for dealing with the trade-off between search and exploitation (exploitation and exploitation), such as reinforcement learning. The algorithm is a method to adjust the probability of choosing the optimal action and the probability of choosing a random action.

        The Boltzmann distribution is one of the important probability distributions in statistical mechanics and physics, which describes how the states of a system are distributed in energy. The Boltzmann distribution is one of the probability distributions that play an important role in machine learning and optimization algorithms, especially in stochastic approaches and Monte Carlo based methods with a wide range of applications, such as The softmax algorithm can be regarded as a generalization of the aforementioned Boltzmann distribution, and the softmax algorithm can be applied to machine learning approaches where the Boltzmann distribution is applied as described above. The application of the softmax algorithm to the bandit problem is described in detail below.

        A Markov Decision Process (MDP) is a mathematical framework in reinforcement learning that is used to model decision-making problems in environments where agents receive rewards associated with states and actions. and Markov properties of the process.

        The algorithms integrating Markov decision processes (MDPs) described in “Overview of Markov decision processes (MDPs), algorithms and implementation examples” and reinforcement learning described in “Overview of reinforcement learning techniques and various implementations” are a combined approach of value-based and policy-based methods.

        Integration of inference and action using Bayesian networks is a method in which agents use probabilistic models to select the most appropriate action while interacting with the environment, and Bayesian networks are a useful approach for representing dependencies between events and handling uncertainty. In this section, the Partially Observed Markov Decision Process (POMDP) is described as an example of an algorithm based on the integration of inference and action using Bayesian networks.

        Thompson Sampling is an algorithm used in probabilistic decision-making problems such as reinforcement learning and multi-armed bandit problems, where the algorithm is used to select the optimal one among multiple alternatives (often called actions or arms) by It is designed to account for uncertainty. It will be particularly useful when the reward for each action is stochastically variable.

        The Upper Confidence Bound (UCB) algorithm is an algorithm for optimal selection among different actions (or arms) in the Multi-Armed Bandit Problem (MBA), considering the uncertainty in the value of the actions, The method aims at selecting the optimal action by appropriately adjusting the trade-off between search and use.

        SARSA (State-Action-Reward-State-Action) is a kind of control algorithm in reinforcement learning, which is mainly classified as a model-free method like Q learning. After observing the resulting reward \(r\), the agent learns a series of transitions until it selects the next action\(a’\) in a new state\(s’\).

        Boltzmann Exploration is a method for balancing search and exploitation in reinforcement learning. Boltzmann Exploration calculates selection probabilities based on action values and uses them to select actions.

        A2C (Advantage Actor-Critic) is an algorithm for reinforcement learning, a type of policy gradient method, which aims to improve the efficiency and stability of learning by simultaneously learning the policy (Actor) and value function (Critic).

        Vanilla Q-Learning is a type of reinforcement learning, which is one of the algorithms used by agents to learn optimal behavior while interacting with their environment. Q-Learning is based on a mathematical model called the Markov Decision Process (MDP), in which the agent learns the value (Q-value) associated with a combination of State and Action, and selects the optimal action based on that Q-value.

        C51, or Categorical DQN, is a deep reinforcement learning algorithm that models the value function as a continuous probability distribution. It has the ability to handle uncertainty by

        Policy Gradient Methods are a type of reinforcement learning that focuses on policy optimization. A policy is a probabilistic strategy that defines what action an agent should choose for a state. Policy gradient methods aim to find the optimal strategy for maximizing reward by directly optimizing the policy.

        Rainbow (“Rainbow: Combining Improvements in Deep Reinforcement Learning”) is a seminal work in the field of deep reinforcement learning that combines several reinforcement learning improvement techniques into an algorithm that improves the performance of DQN (Deep Q-Network) Rainbow outperformed other algorithms on many reinforcement learning tasks and has become one of the benchmark algorithms in subsequent research.

        Prioritized Experience Replay (PER) is a technique for improving Deep Q-Networks (DQN), a type of reinforcement learning. ), and while it is common practice to randomly sample from the experience replay buffer, PER improves on this and becomes a way to preferentially learn important experiences.

        Dueling DQN (Dueling Deep Q-Network) is an algorithm based on Q-learning in reinforcement learning and is a kind of value-based reinforcement learning algorithm. Dueling DQN is an architecture for efficiently estimating Q-values by learning state value functions and advantage functions separately, and this architecture was proposed as an advanced version of Deep Q-Network (DQN).

        Deep Q-Network (DQN) is a combination of deep learning and Q-Learning, and is a reinforcement learning algorithm for problems with high-dimensional state spaces by approximating the Q-function with a neural network. Learning and uses techniques such as replay buffers and fixed target networks to improve learning stability.

        Soft Actor-Critic (SAC) is a type of Reinforcement Learning algorithm that is primarily known as an effective approach for problems with continuous action spaces. Reinforcement Learning) and has several advantages over other algorithms such as Q-learning and Policy Gradients.

        Proximal Policy Optimization (PPO) is a type of reinforcement learning algorithm and one of the policy optimization methods, which is based on the policy gradient method and designed for improved stability and high performance.

        A3C (Asynchronous Advantage Actor-Critic) is a type of deep reinforcement learning algorithm that uses asynchronous learning to train reinforcement learning agents. A3C is particularly suited to tasks in continuous action spaces and has attracted attention for its ability to make effective use of large-scale computational resources.

        Deep Deterministic Policy Gradient (DDPG) is an algorithm that extends the Policy Gradient method (Policy Gradient) in reinforcement learning tasks with continuous state space and continuous action space. deep neural networks to solve reinforcement learning problems in continuous action space.

        REINFORCE (or Monte Carlo Policy Gradient) is a type of reinforcement learning and a policy gradient method. REINFORCE is a method for directly learning policies and finding optimal action selection strategies.

        Actor-Critic is an approach to reinforcement learning that combines policy and value functions (value estimators).

        Trust Region Policy Optimization (TRPO) is a reinforcement learning algorithm, a type of Policy Gradient, that improves policy stability and convergence by optimizing policies under trust region constraints.

        • TRPO-CMA overview, algorithms and implementation examples

        TRPO-CMA (Trust Region Policy Optimization with Covariance Matrix Adaptation) is one of the policy optimization methods in reinforcement learning. It is a combination of TRPO, described in ‘Overview, Algorithms and Implementation Examples of Trust Region Policy Optimisation (TRPO)’, and CMA-ES, described in ‘Overview, Algorithms and Implementation Examples of CMA-ES (Covariance Matrix Adaptation Evolution Strategy)’. The algorithm is designed to efficiently solve complex problems in deep reinforcement learning.

        Double Q-Learning is a type of Q-Learning described in “Overview of Q-Learning, Algorithms, and Examples of Implementations” and is one of the algorithms of reinforcement learning. It reduces the problem of overestimation and improves learning stability by using two Q functions to estimate Q values. This method has been proposed by Richard S. Sutton et al.

        • TD3 (Twin Delayed Deep Deterministic Policy Gradient) overview, algorithms and implementation examples

        TD3 (Twin Delayed Deep Deterministic Policy Gradient) is a type of Actor-Critic method, as described in “Overview, Algorithm and Implementation Examples of A2C (Advantage Actor-Critic)” in the continuous action space in reinforcement learning. TD3 is a type of Actor-Critic method, as described in “Overview of Deep Deterministic Policy Gradient (DDPG) and Examples of Algorithms and Implementations”. TD3 is an extension of the Deep Deterministic Policy Gradient (DDPG) algorithm described in “Deep Deterministic Policy Gradient (DDPG) Overview, Algorithm and Example Implementations” and is aimed at more stable learning and improved performance.

        Inverse Reinforcement Learning (IRL) is a type of reinforcement learning in which the task is to learn the reward function behind the expert’s decisions from the expert’s behavioral data. Usually, in reinforcement learning, a reward function is given and the agent learns the policy that maximizes the reward function. Inverse Reinforcement Learning is the opposite approach, in which the agent analyzes the expert’s behavioral data and aims to learn the reward function corresponding to the expert’s decision making.

        Maximum Entropy Inverse Reinforcement Learning (MaxEnt IRL) is a method for estimating an agent’s reward function from expert behavior data. Typically, inverse reinforcement learning aims to observe how an expert behaves and find a reward function that can explain that behavior; MaxEnt IRL provides a more flexible and general approach by incorporating the Maximum Entropy principle in the estimation of the reward function. Entropy is a measure of the uncertainty of a probability distribution or prediction, and the maximum entropy principle is the idea of choosing the probability distribution with the highest uncertainty.

        Optimal Control-based Inverse Reinforcement Learning (OCIRL) is a method that attempts to estimate the reward function behind an agent’s behavior data when the agent performs a specific task. This approach is based on the theory of optimal control theory. This approach assumes that the agent acts based on optimal control theory.

        ACKTR (Actor-Critic using Kronecker-factored Trust Region) is one of the algorithms of reinforcement learning, based on the idea of the Trust Region Method (Trust Region Policy Optimization, TRPO), It combines Policy Gradient Methods (Policy Gradient Methods) and value function learning, making it particularly suitable for control problems in continuous action spaces.

        Curiosity-Driven Exploration is a general idea and method for improving learning efficiency in reinforcement learning by allowing agents to spontaneously find interesting states and events. This approach aims to allow the agent itself to self-generate information and learn based on it, rather than just a simple reward signal.

        Value Gradients is a method used in the context of reinforcement learning and optimization that computes gradients based on value functions such as state values and action values, and uses these gradients to optimize measures.

        An overview of reinforcement learning and an implementation of a simple MDP model in python will be presented.

        This section describes the method of planning based on the maze environment described in the previous section. Planning requires learning “value evaluation” and “strategy. To do this, it is first necessary to redefine “value” in a way that is consistent with the actual situation.

        Here, we describe an approach using Dynamic Programming. This approach can be used when the transition function and reward function are clear, such as in a maze environment. This method of learning based on the transition function and reward function is called “model-based” learning. The “model” here refers to the environment, and the transition function and reward function that determine the behavior of the environment are the reality.

        In this article, we will discuss the model-free method. Model-free is a method in which the agent accumulates experience by moving itself and learns from that experience. Unlike the model-based methods described above, it is assumed that information on the environment, i.e., transition function and reward function, is not known.

        There are three points to be considered in utilizing the “experience” of the agent’s actions. (1) accumulation and balance of experience, (2) whether to revise plans based on actual results or forecasts, and (3) whether to use experience for value assessment or strategy update.

        In this article, we discuss the trade-off between behavior modification based on actual performance and behavior modification based on prediction. We will discuss the Monte Carlo method for the former and the Temporal Difference Learning (TD) method for the latter. The Multi-step Learning method and the TD(λ) method (TD-Lambda method) are also described as methods that fall between the two.

        In this article, I will discuss the difference between using experience for updating “value assessment” or “strategy”. This is the same as the difference between Value-based and Policy-based. We will look at the difference between the two, and also discuss a two-fold approach to updating both.

        The major difference between value-based and policy-based learning is the criterion for action selection: value-based learning determines actions to move to the state with the greatest value, while policy-based learning determines actions based on strategy. The former criterion, which does not use strategy, is called Off-policy (no strategy = Off). In contrast, a school building that assumes a strategy is called On-policy.

        Take Q-Learning as an example: the target of Q-Learning updates is “value evaluation,” and the criteria for action selection is Off-policy. This is evident from the fact that Q-Learning is implemented in such a way that it “takes action a to maximize value” (max(self.G[n-state])). In contrast, there is a method where the update target is “strategy” and the criterion is “on-policy”. That is SARSA (State-Action-Reward-State-Action).

          In this article, we will discuss how to implement value functions and strategies with parameterized functions. This will allow us to deal with continuous states and actions that are difficult to handle in table management.

          This time, we describe the implementation by pyhton in the framework of applying deep learning to reinforcement learning.

          In this article, I will describe a method of replacing the value evaluation by a function with parameters, which is performed by a table (Q[s][a], Q-table) as described in “Implementation of model-free reinforcement learning in python (1) epsilon-Greedy method” etc. The function to perform value evaluation is called value function. The function that evaluates the value is called a value function, and learning (estimating) the value function is called Value Function Approximation (or simply Function Approximation). In value function-based methods, action selection is based on the output of the value function. In other words, it is a Value-based method.

          In this article, we will create an agent that decides its action based on the value function and attack the CartPole environment, which is a popular environment in the OpenAI Gym and is used in various samples. A neural network is used for the value function.

          In this article, we describe a game strategy using CNN. The basic mechanism is almost the same as the aforementioned, but the environment is changed in order to experience the advantage of direct screen input. This time, as a specific subject, we will describe Catcher, a game in which vol-catching is performed.

          The Deep-Q-Network we have implemented here is currently undergoing many improvements, and Deep Mind, the company that introduced the Deep-Q-Network, has published a model called Rainbow that incorporates six excellent improvements (adding the Deep-Q-Network together makes a total of seven, or seven colors of Rainbow).

          A strategy can also be represented by a function with parameters. It is a function that takes a state as an argument and outputs an action or action probability. However, it is not easy to update the parameters of the strategy. In value evaluation, there was a straightforward goal of bringing the estimated value closer to the actual value. However, the action or action probability output from the strategy cannot be directly compared to the value that can be calculated. The expected value of the value would be the learning tip in this case.

          Just as we applied DNN to the value function, we can apply DNN to the strategy function. Specifically, it is a function that takes the game screen as input and outputs actions and action probabilities.

          There were several variations of Policy Gradient, but here we describe a method called Actor Critic (A2C), which uses Advantage. The name “A2C” itself means only “Advantage Actor Critic,” but the method generally referred to as “A2C” includes methods that collect experience in a distributed environment in parallel. In this section, only the purely “A2C” part is implemented, and the distributed collection is only explained.

          A3C (Asynchronous Advantage Actor Critic)” was published before A2C, and it uses the same distributed environment as A2C. The agent not only collects experience in each environment, but also learns. This is “asynchronous” learning (in each environment). However, A “2 “C was created because it was thought that sufficient or higher accuracy could be achieved without asynchronous learning, i.e., two “A “s were sufficient instead of three. Therefore, although it is not Asynchronous learning, the collection of experience in a distributed environment remains.

          In “Applying Neural Networks to Reinforcement Learning: Applying Deep Learning to Strategies: Advanced Actor Critic (A2C),” it was mentioned that “Policy Gradient-based methods sometimes have unstable execution results,” and a method to improve this has been proposed. TRPO/PPO, along with the aforementioned A2C/A3C, are currently used as standard algorithms.

          In the application of deep learning to reinforcement learning, “value evaluation” and “strategy” were each implemented as a function, and the function was optimized using neural networks. The correlation diagram of the main methods is shown below. There are three negative aspects of reinforcement learning as follows. (1) poor sample efficiency, (2) falling into locally optimal behavior, sometimes overlearning, and (3) poor reproducibility.

          In this article, we will discuss methods to overcome the three weaknesses of reinforcement learning: “poor sample efficiency,” “falling into locally optimal behavior, often overlearning,” and “poor reproducibility. In particular, “poor sample efficiency” has become a major issue, and various countermeasures have been proposed. There are various approaches to these problems, but this time we will focus on “improvement of environment recognition.

          In “Overview of Weaknesses of Deep Reinforcement Learning and Countermeasures and Two Approaches for Improving Environment Recognition,” I described methods for overcoming three weaknesses of deep reinforcement learning: “poor sample efficiency,” “falling into locally optimal behavior,” “often overlearning,” and “poor reproducibility. In particular, we focused on “improvement of environment recognition” as a countermeasure to the main issue of “poor sample efficiency. In this report, we describe the implementation of these methods.

          • Overcoming Weaknesses in Deep Reinforcement Learning Dealing with Low Reproducibility: Evolutionary Strategies

          Deep reinforcement learning has the problem of “unstable learning,” which has led to low reproducibility. Not only deep reinforcement learning, but also deep learning generally uses a learning method called the gradient method. Recently, evolutionary strategies (Evolution Startegies) have attracted attention as an alternative learning method to the gradient method. Evolutionary strategies are a classical method proposed at the same time as genetic algorithms and are very simple.

          On a desktop PC (64-bit Corei-7 8GM), the above training can be done in less than one hour, which is much shorter than the usual reinforcement learning, and the reward can be obtained without a GPU. Optimization by evolutionary strategy is still under research, but it has the potential to rival the gradient method in the future. Research on the use or combination of other optimization algorithms to improve the reproducibility of reinforcement learning, rather than improving the gradient method, may be developed in the future.

          • Overcoming Weaknesses in Deep Reinforcement Learning Dealing with Locally Optimal Behavior/Overlearning: Inverse Reinforcement Learning

          Continuing from the previous article, this time we will discuss how to deal with locally optimal behavior and over-learning. Here, we discuss inverse reinforcement learning.

          Inversed Reinforcement Learning (IRL) does not imitate the expert’s behavior but estimates the reward function behind the behavior. There are three advantages to estimating the reward function: first, it eliminates the need to design rewards, thereby preventing unintended behavior; second, it can be used for transfer to other tasks, and if the reward function is close, it can be used for learning another task (e.g., learning another game of the same genre); and third, it can be used for human learning. Third, it can be used to understand human (and animal) behavior.

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