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Dive into Deep Learning — Dive into Deep Learning 1.0.3 documentation

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Dive into Deep Learning — Dive into Deep Learning 1.0.3 documentation

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H1 H2 H3 H4 H5 H6
1 9 19 1 0 0
  • [H1] Dive into Deep Learning¶
  • [H2] Dive into Deep Learning
  • [H2] Authors
  • [H2] Vol.2 Chapter Authors
  • [H2] Framework Adaptation Authors
  • [H2] Each section is an executable Jupyter notebook
  • [H2] Mathematics + Figures + Code
  • [H2] Active community support
  • [H2] D2L as a textbook or a reference book
  • [H2] Table of contents
  • [H3] Aston Zhang
  • [H3] Zack C. Lipton
  • [H3] Mu Li
  • [H3] Alex J. Smola
  • [H3] Pratik Chaudhari
  • [H3] Rasool Fakoor
  • [H3] Kavosh Asadi
  • [H3] Andrew Gordon Wilson
  • [H3] Aaron Klein
  • [H3] Matthias Seeger
  • [H3] Cedric Archambeau
  • [H3] Shuai Zhang
  • [H3] Yi Tay
  • [H3] Brent Werness
  • [H3] Rachel Hu
  • [H3] Anirudh Dagar
  • [H3] Yuan Tang
  • [H3] We thank all the community contributorsfor making this open source book better for everyone.
  • [H3] BibTeX entry for citing the book
  • [H4] Contribute to the book

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Anker Type Juice
Preface Intern Sender Juice
Installation Intern Sender Juice
Notation Intern Sender Juice
1. Introduction Intern Sender Juice
2. Preliminaries Intern Sender Juice
2.1. Data Manipulation Intern Sender Juice
2.2. Data Preprocessing Intern Sender Juice
2.3. Linear Algebra Intern Sender Juice
2.4. Calculus Intern Sender Juice
2.5. Automatic Differentiation Intern Sender Juice
2.6. Probability and Statistics Intern Sender Juice
2.7. Documentation Intern Sender Juice
3. Linear Neural Networks for Regression Intern Sender Juice
3.1. Linear Regression Intern Sender Juice
3.2. Object-Oriented Design for Implementation Intern Sender Juice
3.3. Synthetic Regression Data Intern Sender Juice
3.4. Linear Regression Implementation from Scratch Intern Sender Juice
3.5. Concise Implementation of Linear Regression Intern Sender Juice
3.6. Generalization Intern Sender Juice
3.7. Weight Decay Intern Sender Juice
4. Linear Neural Networks for Classification Intern Sender Juice
4.1. Softmax Regression Intern Sender Juice
4.2. The Image Classification Dataset Intern Sender Juice
4.3. The Base Classification Model Intern Sender Juice
4.4. Softmax Regression Implementation from Scratch Intern Sender Juice
4.5. Concise Implementation of Softmax Regression Intern Sender Juice
4.6. Generalization in Classification Intern Sender Juice
4.7. Environment and Distribution Shift Intern Sender Juice
5. Multilayer Perceptrons Intern Sender Juice
5.1. Multilayer Perceptrons Intern Sender Juice
5.2. Implementation of Multilayer Perceptrons Intern Sender Juice
5.3. Forward Propagation, Backward Propagation, and Computational Graphs Intern Sender Juice
5.4. Numerical Stability and Initialization Intern Sender Juice
5.5. Generalization in Deep Learning Intern Sender Juice
5.6. Dropout Intern Sender Juice
5.7. Predicting House Prices on Kaggle Intern Sender Juice
6. Builders’ Guide Intern Sender Juice
6.1. Layers and Modules Intern Sender Juice
6.2. Parameter Management Intern Sender Juice
6.3. Parameter Initialization Intern Sender Juice
6.4. Lazy Initialization Intern Sender Juice
6.5. Custom Layers Intern Sender Juice
6.6. File I/O Intern Sender Juice
6.7. GPUs Intern Sender Juice
7. Convolutional Neural Networks Intern Sender Juice
7.1. From Fully Connected Layers to Convolutions Intern Sender Juice
7.2. Convolutions for Images Intern Sender Juice
7.3. Padding and Stride Intern Sender Juice
7.4. Multiple Input and Multiple Output Channels Intern Sender Juice
7.5. Pooling Intern Sender Juice
7.6. Convolutional Neural Networks (LeNet) Intern Sender Juice
8. Modern Convolutional Neural Networks Intern Sender Juice
8.1. Deep Convolutional Neural Networks (AlexNet) Intern Sender Juice
8.2. Networks Using Blocks (VGG) Intern Sender Juice
8.3. Network in Network (NiN) Intern Sender Juice
8.4. Multi-Branch Networks (GoogLeNet) Intern Sender Juice
8.5. Batch Normalization Intern Sender Juice
8.6. Residual Networks (ResNet) and ResNeXt Intern Sender Juice
8.7. Densely Connected Networks (DenseNet) Intern Sender Juice
8.8. Designing Convolution Network Architectures Intern Sender Juice
9. Recurrent Neural Networks Intern Sender Juice
9.1. Working with Sequences Intern Sender Juice
9.2. Converting Raw Text into Sequence Data Intern Sender Juice
9.3. Language Models Intern Sender Juice
9.4. Recurrent Neural Networks Intern Sender Juice
9.5. Recurrent Neural Network Implementation from Scratch Intern Sender Juice
9.6. Concise Implementation of Recurrent Neural Networks Intern Sender Juice
9.7. Backpropagation Through Time Intern Sender Juice
10. Modern Recurrent Neural Networks Intern Sender Juice
10.1. Long Short-Term Memory (LSTM) Intern Sender Juice
10.2. Gated Recurrent Units (GRU) Intern Sender Juice
10.3. Deep Recurrent Neural Networks Intern Sender Juice
10.4. Bidirectional Recurrent Neural Networks Intern Sender Juice
10.5. Machine Translation and the Dataset Intern Sender Juice
10.6. The Encoder–Decoder Architecture Intern Sender Juice
10.7. Sequence-to-Sequence Learning for Machine Translation Intern Sender Juice
10.8. Beam Search Intern Sender Juice
11. Attention Mechanisms and Transformers Intern Sender Juice
11.1. Queries, Keys, and Values Intern Sender Juice
11.2. Attention Pooling by Similarity Intern Sender Juice
11.3. Attention Scoring Functions Intern Sender Juice
11.4. The Bahdanau Attention Mechanism Intern Sender Juice
11.5. Multi-Head Attention Intern Sender Juice
11.6. Self-Attention and Positional Encoding Intern Sender Juice
11.7. The Transformer Architecture Intern Sender Juice
11.8. Transformers for Vision Intern Sender Juice
11.9. Large-Scale Pretraining with Transformers Intern Sender Juice
12. Optimization Algorithms Intern Sender Juice
12.1. Optimization and Deep Learning Intern Sender Juice
12.2. Convexity Intern Sender Juice
12.3. Gradient Descent Intern Sender Juice
12.4. Stochastic Gradient Descent Intern Sender Juice
12.5. Minibatch Stochastic Gradient Descent Intern Sender Juice
12.6. Momentum Intern Sender Juice
12.7. Adagrad Intern Sender Juice
12.8. RMSProp Intern Sender Juice
12.9. Adadelta Intern Sender Juice
12.10. Adam Intern Sender Juice
12.11. Learning Rate Scheduling Intern Sender Juice
13. Computational Performance Intern Sender Juice
13.1. Compilers and Interpreters Intern Sender Juice
13.2. Asynchronous Computation Intern Sender Juice
13.3. Automatic Parallelism Intern Sender Juice
13.4. Hardware Intern Sender Juice
13.5. Training on Multiple GPUs Intern Sender Juice
13.6. Concise Implementation for Multiple GPUs Intern Sender Juice
13.7. Parameter Servers Intern Sender Juice
14. Computer Vision Intern Sender Juice
14.1. Image Augmentation Intern Sender Juice
14.2. Fine-Tuning Intern Sender Juice
14.3. Object Detection and Bounding Boxes Intern Sender Juice
14.4. Anchor Boxes Intern Sender Juice
14.5. Multiscale Object Detection Intern Sender Juice
14.6. The Object Detection Dataset Intern Sender Juice
14.7. Single Shot Multibox Detection Intern Sender Juice
14.8. Region-based CNNs (R-CNNs) Intern Sender Juice
14.9. Semantic Segmentation and the Dataset Intern Sender Juice
14.10. Transposed Convolution Intern Sender Juice
14.11. Fully Convolutional Networks Intern Sender Juice
14.12. Neural Style Transfer Intern Sender Juice
14.13. Image Classification (CIFAR-10) on Kaggle Intern Sender Juice
14.14. Dog Breed Identification (ImageNet Dogs) on Kaggle Intern Sender Juice
15. Natural Language Processing: Pretraining Intern Sender Juice
15.1. Word Embedding (word2vec) Intern Sender Juice
15.2. Approximate Training Intern Sender Juice
15.3. The Dataset for Pretraining Word Embeddings Intern Sender Juice
15.4. Pretraining word2vec Intern Sender Juice
15.5. Word Embedding with Global Vectors (GloVe) Intern Sender Juice
15.6. Subword Embedding Intern Sender Juice
15.7. Word Similarity and Analogy Intern Sender Juice
15.8. Bidirectional Encoder Representations from Transformers (BERT) Intern Sender Juice
15.9. The Dataset for Pretraining BERT Intern Sender Juice
15.10. Pretraining BERT Intern Sender Juice
16. Natural Language Processing: Applications Intern Sender Juice
16.1. Sentiment Analysis and the Dataset Intern Sender Juice
16.2. Sentiment Analysis: Using Recurrent Neural Networks Intern Sender Juice
16.3. Sentiment Analysis: Using Convolutional Neural Networks Intern Sender Juice
16.4. Natural Language Inference and the Dataset Intern Sender Juice
16.5. Natural Language Inference: Using Attention Intern Sender Juice
16.6. Fine-Tuning BERT for Sequence-Level and Token-Level Applications Intern Sender Juice
16.7. Natural Language Inference: Fine-Tuning BERT Intern Sender Juice
17. Reinforcement Learning Intern Sender Juice
17.1. Markov Decision Process (MDP) Intern Sender Juice
17.2. Value Iteration Intern Sender Juice
17.3. Q-Learning Intern Sender Juice
18. Gaussian Processes Intern Sender Juice
18.1. Introduction to Gaussian Processes Intern Sender Juice
18.2. Gaussian Process Priors Intern Sender Juice
18.3. Gaussian Process Inference Intern Sender Juice
19. Hyperparameter Optimization Intern Sender Juice
19.1. What Is Hyperparameter Optimization? Intern Sender Juice
19.2. Hyperparameter Optimization API Intern Sender Juice
19.3. Asynchronous Random Search Intern Sender Juice
19.4. Multi-Fidelity Hyperparameter Optimization Intern Sender Juice
19.5. Asynchronous Successive Halving Intern Sender Juice
20. Generative Adversarial Networks Intern Sender Juice
20.1. Generative Adversarial Networks Intern Sender Juice
20.2. Deep Convolutional Generative Adversarial Networks Intern Sender Juice
21. Recommender Systems Intern Sender Juice
21.1. Overview of Recommender Systems Intern Sender Juice
21.2. The MovieLens Dataset Intern Sender Juice
21.3. Matrix Factorization Intern Sender Juice
21.4. AutoRec: Rating Prediction with Autoencoders Intern Sender Juice
21.5. Personalized Ranking for Recommender Systems Intern Sender Juice
21.6. Neural Collaborative Filtering for Personalized Ranking Intern Sender Juice
21.7. Sequence-Aware Recommender Systems Intern Sender Juice
21.8. Feature-Rich Recommender Systems Intern Sender Juice
21.9. Factorization Machines Intern Sender Juice
21.10. Deep Factorization Machines Intern Sender Juice
22. Appendix: Mathematics for Deep Learning Intern Sender Juice
22.1. Geometry and Linear Algebraic Operations Intern Sender Juice
22.2. Eigendecompositions Intern Sender Juice
22.3. Single Variable Calculus Intern Sender Juice
22.4. Multivariable Calculus Intern Sender Juice
22.5. Integral Calculus Intern Sender Juice
22.6. Random Variables Intern Sender Juice
22.7. Maximum Likelihood Intern Sender Juice
22.8. Distributions Intern Sender Juice
22.9. Naive Bayes Intern Sender Juice
22.10. Statistics Intern Sender Juice
22.11. Information Theory Intern Sender Juice
23. Appendix: Tools for Deep Learning Intern Sender Juice
23.1. Using Jupyter Notebooks Intern Sender Juice
23.2. Using Amazon SageMaker Intern Sender Juice
23.3. Using AWS EC2 Instances Intern Sender Juice
23.4. Using Google Colab Intern Sender Juice
23.5. Selecting Servers and GPUs Intern Sender Juice
23.6. Contributing to This Book Intern Sender Juice
23.7. Utility Functions and Classes Intern Sender Juice
References Intern Sender Juice
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1.1. A Motivating Example Intern Sender Juice
1.2. Key Components Intern Sender Juice
1.3. Kinds of Machine Learning Problems Intern Sender Juice
1.4. Roots Intern Sender Juice
1.5. The Road to Deep Learning Intern Sender Juice
1.6. Success Stories Intern Sender Juice
1.7. The Essence of Deep Learning Intern Sender Juice
1.8. Summary Intern Sender Juice
1.9. Exercises Intern Sender Juice

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