Dive into Deep Learning (EN)

Preface1 章

1. 01Prefacemarkdown

Installation1 章

1. 02Installationmarkdown

Notation1 章

1. 03Notationmarkdown

Introduction1 章

1. 04Introductionmarkdown

Preliminaries8 章

1. 05Preliminariesmarkdown
2. 06Data Manipulationmarkdown
3. 07Data Preprocessingmarkdown
4. 08Linear Algebramarkdown
5. 09Calculusmarkdown
6. 10Automatic Differentiationmarkdown
7. 11Probability and Statisticsmarkdown
8. 12Documentationmarkdown

Linear Neural Networks for Regression8 章

1. 13Linear Neural Networks for Regressionmarkdown
2. 14Linear Regressionmarkdown
3. 15Object-Oriented Design for Implementationmarkdown
4. 16Synthetic Regression Datamarkdown
5. 17Linear Regression Implementation from Scratchmarkdown
6. 18Concise Implementation of Linear Regressionmarkdown
7. 19Generalizationmarkdown
8. 20Weight Decaymarkdown

Linear Neural Networks for Classification8 章

1. 21Linear Neural Networks for Classificationmarkdown
2. 22Softmax Regressionmarkdown
3. 23The Image Classification Datasetmarkdown
4. 24The Base Classification Modelmarkdown
5. 25Softmax Regression Implementation from Scratchmarkdown
6. 26Concise Implementation of Softmax Regressionmarkdown
7. 27Generalization in Classificationmarkdown
8. 28Environment and Distribution Shiftmarkdown

Multilayer Perceptrons8 章

1. 29Multilayer Perceptronsmarkdown
2. 30Multilayer Perceptronsmarkdown
3. 31Implementation of Multilayer Perceptronsmarkdown
4. 32Forward Propagation, Backward Propagation, and Computational Graphsmarkdown
5. 33Numerical Stability and Initializationmarkdown
6. 34Generalization in Deep Learningmarkdown
7. 35Dropoutmarkdown
8. 36Predicting House Prices on Kagglemarkdown

Builders' Guide8 章

1. 37Builders' Guidemarkdown
2. 38Layers and Modulesmarkdown
3. 39Parameter Managementmarkdown
4. 40Parameter Initializationmarkdown
5. 41Lazy Initializationmarkdown
6. 42Custom Layersmarkdown
7. 43File I/Omarkdown
8. 44GPUsmarkdown

Convolutional Neural Networks7 章

1. 45Convolutional Neural Networksmarkdown
2. 46From Fully Connected Layers to Convolutionsmarkdown
3. 47Convolutions for Imagesmarkdown
4. 48Padding and Stridemarkdown
5. 49Multiple Input and Multiple Output Channelsmarkdown
6. 50Poolingmarkdown
7. 51Convolutional Neural Networks (LeNet)markdown

Modern Convolutional Neural Networks9 章

1. 52Modern Convolutional Neural Networksmarkdown
2. 53Deep Convolutional Neural Networks (AlexNet)markdown
3. 54Networks Using Blocks (VGG)markdown
4. 55Network in Network (NiN)markdown
5. 56Multi-Branch Networks (GoogLeNet)markdown
6. 57Batch Normalizationmarkdown
7. 58Residual Networks (ResNet) and ResNeXtmarkdown
8. 59Densely Connected Networks (DenseNet)markdown

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Recurrent Neural Networks8 章

1. 61Recurrent Neural Networksmarkdown
2. 62Working with Sequencesmarkdown
3. 63Converting Raw Text into Sequence Datamarkdown
4. 64Language Modelsmarkdown
5. 65Recurrent Neural Networksmarkdown
6. 66Recurrent Neural Network Implementation from Scratchmarkdown
7. 67Concise Implementation of Recurrent Neural Networksmarkdown
8. 68Backpropagation Through Timemarkdown

Modern Recurrent Neural Networks9 章

1. 69Modern Recurrent Neural Networksmarkdown
2. 70Long Short-Term Memory (LSTM)markdown
3. 71Gated Recurrent Units (GRU)markdown
4. 72Deep Recurrent Neural Networksmarkdown
5. 73Bidirectional Recurrent Neural Networksmarkdown
6. 74Machine Translation and the Datasetmarkdown
7. 75The Encoder--Decoder Architecturemarkdown
8. 76Sequence-to-Sequence Learning for Machine Translationmarkdown

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Attention Mechanisms and Transformers10 章

1. 78Attention Mechanisms and Transformersmarkdown
2. 79Queries, Keys, and Valuesmarkdown
3. 80Attention Pooling by Similaritymarkdown
4. 81Attention Scoring Functionsmarkdown
5. 82The Bahdanau Attention Mechanismmarkdown
6. 83Multi-Head Attentionmarkdown
7. 84Self-Attention and Positional Encodingmarkdown
8. 85The Transformer Architecturemarkdown

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Optimization Algorithms12 章

1. 88Optimization Algorithmsmarkdown
2. 89Optimization and Deep Learningmarkdown
3. 90Convexitymarkdown
4. 91Gradient Descentmarkdown
5. 92Stochastic Gradient Descentmarkdown
6. 93Minibatch Stochastic Gradient Descentmarkdown
7. 94Momentummarkdown
8. 95Adagradmarkdown

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Computational Performance8 章

1. 100Computational Performancemarkdown
2. 101Compilers and Interpretersmarkdown
3. 102Asynchronous Computationmarkdown
4. 103Automatic Parallelismmarkdown
5. 104Hardwaremarkdown
6. 105Training on Multiple GPUsmarkdown
7. 106Concise Implementation for Multiple GPUsmarkdown
8. 107Parameter Serversmarkdown

Computer Vision15 章

1. 108Computer Visionmarkdown
2. 109Image Augmentationmarkdown
3. 110Fine-Tuningmarkdown
4. 111Object Detection and Bounding Boxesmarkdown
5. 112Anchor Boxesmarkdown
6. 113Multiscale Object Detectionmarkdown
7. 114The Object Detection Datasetmarkdown
8. 115Single Shot Multibox Detectionmarkdown

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Natural Language Processing: Pretraining11 章

1. 123Natural Language Processing: Pretrainingmarkdown
2. 124Word Embedding (word2vec)markdown
3. 125Approximate Trainingmarkdown
4. 126The Dataset for Pretraining Word Embeddingsmarkdown
5. 127Pretraining word2vecmarkdown
6. 128Word Embedding with Global Vectors (GloVe)markdown
7. 129Subword Embeddingmarkdown
8. 130Word Similarity and Analogymarkdown

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Natural Language Processing: Applications8 章

1. 134Natural Language Processing: Applicationsmarkdown
2. 135Sentiment Analysis and the Datasetmarkdown
3. 136Sentiment Analysis: Using Recurrent Neural Networksmarkdown
4. 137Sentiment Analysis: Using Convolutional Neural Networksmarkdown
5. 138Natural Language Inference and the Datasetmarkdown
6. 139Natural Language Inference: Using Attentionmarkdown
7. 140Fine-Tuning BERT for Sequence-Level and Token-Level Applicationsmarkdown
8. 141Natural Language Inference: Fine-Tuning BERTmarkdown

Reinforcement Learning4 章

1. 142Reinforcement Learningmarkdown
2. 143Markov Decision Process (MDP)markdown
3. 144Value Iterationmarkdown
4. 145Q-Learningmarkdown

Gaussian Processes4 章

1. 146Gaussian Processesmarkdown
2. 147Introduction to Gaussian Processesmarkdown
3. 148Gaussian Process Priorsmarkdown
4. 149Gaussian Process Inferencemarkdown

Hyperparameter Optimization5 章

1. 150Hyperparameter Optimizationmarkdown
2. 151What Is Hyperparameter Optimization?markdown
3. 152Hyperparameter Optimization APImarkdown
4. 153Multi-Fidelity Hyperparameter Optimizationmarkdown
5. 154Asynchronous Successive Halvingmarkdown

Generative Adversarial Networks3 章

1. 155Generative Adversarial Networksmarkdown
2. 156Generative Adversarial Networksmarkdown
3. 157Deep Convolutional Generative Adversarial Networksmarkdown

Recommender Systems11 章

1. 158Recommender Systemsmarkdown
2. 159Overview of Recommender Systemsmarkdown
3. 160The MovieLens Datasetmarkdown
4. 161Matrix Factorizationmarkdown
5. 162AutoRec: Rating Prediction with Autoencodersmarkdown
6. 163Personalized Ranking for Recommender Systemsmarkdown
7. 164Neural Collaborative Filtering for Personalized Rankingmarkdown
8. 165Sequence-Aware Recommender Systemsmarkdown

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Appendix: Mathematics for Deep Learning12 章

1. 169Appendix: Mathematics for Deep Learningmarkdown
2. 170Geometry and Linear Algebraic Operationsmarkdown
3. 171Eigendecompositionsmarkdown
4. 172Single Variable Calculusmarkdown
5. 173Multivariable Calculusmarkdown
6. 174Integral Calculusmarkdown
7. 175Random Variablesmarkdown
8. 176Maximum Likelihoodmarkdown

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Appendix: Tools for Deep Learning9 章

1. 181Appendix: Tools for Deep Learningmarkdown
2. 182Using Jupyter Notebooksmarkdown
3. 183Using Amazon SageMakermarkdown
4. 184Using AWS EC2 Instancesmarkdown
5. 185Using Google Colabmarkdown
6. 186Selecting Servers and GPUsmarkdown
7. 187Contributing to This Bookmarkdown
8. 188Utility Functions and Classesmarkdown

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