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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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Anchor Typ Juice
Preface Interna Passing Juice
Installation Interna Passing Juice
Notation Interna Passing Juice
1. Introduction Interna Passing Juice
2. Preliminaries Interna Passing Juice
2.1. Data Manipulation Interna Passing Juice
2.2. Data Preprocessing Interna Passing Juice
2.3. Linear Algebra Interna Passing Juice
2.4. Calculus Interna Passing Juice
2.5. Automatic Differentiation Interna Passing Juice
2.6. Probability and Statistics Interna Passing Juice
2.7. Documentation Interna Passing Juice
3. Linear Neural Networks for Regression Interna Passing Juice
3.1. Linear Regression Interna Passing Juice
3.2. Object-Oriented Design for Implementation Interna Passing Juice
3.3. Synthetic Regression Data Interna Passing Juice
3.4. Linear Regression Implementation from Scratch Interna Passing Juice
3.5. Concise Implementation of Linear Regression Interna Passing Juice
3.6. Generalization Interna Passing Juice
3.7. Weight Decay Interna Passing Juice
4. Linear Neural Networks for Classification Interna Passing Juice
4.1. Softmax Regression Interna Passing Juice
4.2. The Image Classification Dataset Interna Passing Juice
4.3. The Base Classification Model Interna Passing Juice
4.4. Softmax Regression Implementation from Scratch Interna Passing Juice
4.5. Concise Implementation of Softmax Regression Interna Passing Juice
4.6. Generalization in Classification Interna Passing Juice
4.7. Environment and Distribution Shift Interna Passing Juice
5. Multilayer Perceptrons Interna Passing Juice
5.1. Multilayer Perceptrons Interna Passing Juice
5.2. Implementation of Multilayer Perceptrons Interna Passing Juice
5.3. Forward Propagation, Backward Propagation, and Computational Graphs Interna Passing Juice
5.4. Numerical Stability and Initialization Interna Passing Juice
5.5. Generalization in Deep Learning Interna Passing Juice
5.6. Dropout Interna Passing Juice
5.7. Predicting House Prices on Kaggle Interna Passing Juice
6. Builders’ Guide Interna Passing Juice
6.1. Layers and Modules Interna Passing Juice
6.2. Parameter Management Interna Passing Juice
6.3. Parameter Initialization Interna Passing Juice
6.4. Lazy Initialization Interna Passing Juice
6.5. Custom Layers Interna Passing Juice
6.6. File I/O Interna Passing Juice
6.7. GPUs Interna Passing Juice
7. Convolutional Neural Networks Interna Passing Juice
7.1. From Fully Connected Layers to Convolutions Interna Passing Juice
7.2. Convolutions for Images Interna Passing Juice
7.3. Padding and Stride Interna Passing Juice
7.4. Multiple Input and Multiple Output Channels Interna Passing Juice
7.5. Pooling Interna Passing Juice
7.6. Convolutional Neural Networks (LeNet) Interna Passing Juice
8. Modern Convolutional Neural Networks Interna Passing Juice
8.1. Deep Convolutional Neural Networks (AlexNet) Interna Passing Juice
8.2. Networks Using Blocks (VGG) Interna Passing Juice
8.3. Network in Network (NiN) Interna Passing Juice
8.4. Multi-Branch Networks (GoogLeNet) Interna Passing Juice
8.5. Batch Normalization Interna Passing Juice
8.6. Residual Networks (ResNet) and ResNeXt Interna Passing Juice
8.7. Densely Connected Networks (DenseNet) Interna Passing Juice
8.8. Designing Convolution Network Architectures Interna Passing Juice
9. Recurrent Neural Networks Interna Passing Juice
9.1. Working with Sequences Interna Passing Juice
9.2. Converting Raw Text into Sequence Data Interna Passing Juice
9.3. Language Models Interna Passing Juice
9.4. Recurrent Neural Networks Interna Passing Juice
9.5. Recurrent Neural Network Implementation from Scratch Interna Passing Juice
9.6. Concise Implementation of Recurrent Neural Networks Interna Passing Juice
9.7. Backpropagation Through Time Interna Passing Juice
10. Modern Recurrent Neural Networks Interna Passing Juice
10.1. Long Short-Term Memory (LSTM) Interna Passing Juice
10.2. Gated Recurrent Units (GRU) Interna Passing Juice
10.3. Deep Recurrent Neural Networks Interna Passing Juice
10.4. Bidirectional Recurrent Neural Networks Interna Passing Juice
10.5. Machine Translation and the Dataset Interna Passing Juice
10.6. The Encoder–Decoder Architecture Interna Passing Juice
10.7. Sequence-to-Sequence Learning for Machine Translation Interna Passing Juice
10.8. Beam Search Interna Passing Juice
11. Attention Mechanisms and Transformers Interna Passing Juice
11.1. Queries, Keys, and Values Interna Passing Juice
11.2. Attention Pooling by Similarity Interna Passing Juice
11.3. Attention Scoring Functions Interna Passing Juice
11.4. The Bahdanau Attention Mechanism Interna Passing Juice
11.5. Multi-Head Attention Interna Passing Juice
11.6. Self-Attention and Positional Encoding Interna Passing Juice
11.7. The Transformer Architecture Interna Passing Juice
11.8. Transformers for Vision Interna Passing Juice
11.9. Large-Scale Pretraining with Transformers Interna Passing Juice
12. Optimization Algorithms Interna Passing Juice
12.1. Optimization and Deep Learning Interna Passing Juice
12.2. Convexity Interna Passing Juice
12.3. Gradient Descent Interna Passing Juice
12.4. Stochastic Gradient Descent Interna Passing Juice
12.5. Minibatch Stochastic Gradient Descent Interna Passing Juice
12.6. Momentum Interna Passing Juice
12.7. Adagrad Interna Passing Juice
12.8. RMSProp Interna Passing Juice
12.9. Adadelta Interna Passing Juice
12.10. Adam Interna Passing Juice
12.11. Learning Rate Scheduling Interna Passing Juice
13. Computational Performance Interna Passing Juice
13.1. Compilers and Interpreters Interna Passing Juice
13.2. Asynchronous Computation Interna Passing Juice
13.3. Automatic Parallelism Interna Passing Juice
13.4. Hardware Interna Passing Juice
13.5. Training on Multiple GPUs Interna Passing Juice
13.6. Concise Implementation for Multiple GPUs Interna Passing Juice
13.7. Parameter Servers Interna Passing Juice
14. Computer Vision Interna Passing Juice
14.1. Image Augmentation Interna Passing Juice
14.2. Fine-Tuning Interna Passing Juice
14.3. Object Detection and Bounding Boxes Interna Passing Juice
14.4. Anchor Boxes Interna Passing Juice
14.5. Multiscale Object Detection Interna Passing Juice
14.6. The Object Detection Dataset Interna Passing Juice
14.7. Single Shot Multibox Detection Interna Passing Juice
14.8. Region-based CNNs (R-CNNs) Interna Passing Juice
14.9. Semantic Segmentation and the Dataset Interna Passing Juice
14.10. Transposed Convolution Interna Passing Juice
14.11. Fully Convolutional Networks Interna Passing Juice
14.12. Neural Style Transfer Interna Passing Juice
14.13. Image Classification (CIFAR-10) on Kaggle Interna Passing Juice
14.14. Dog Breed Identification (ImageNet Dogs) on Kaggle Interna Passing Juice
15. Natural Language Processing: Pretraining Interna Passing Juice
15.1. Word Embedding (word2vec) Interna Passing Juice
15.2. Approximate Training Interna Passing Juice
15.3. The Dataset for Pretraining Word Embeddings Interna Passing Juice
15.4. Pretraining word2vec Interna Passing Juice
15.5. Word Embedding with Global Vectors (GloVe) Interna Passing Juice
15.6. Subword Embedding Interna Passing Juice
15.7. Word Similarity and Analogy Interna Passing Juice
15.8. Bidirectional Encoder Representations from Transformers (BERT) Interna Passing Juice
15.9. The Dataset for Pretraining BERT Interna Passing Juice
15.10. Pretraining BERT Interna Passing Juice
16. Natural Language Processing: Applications Interna Passing Juice
16.1. Sentiment Analysis and the Dataset Interna Passing Juice
16.2. Sentiment Analysis: Using Recurrent Neural Networks Interna Passing Juice
16.3. Sentiment Analysis: Using Convolutional Neural Networks Interna Passing Juice
16.4. Natural Language Inference and the Dataset Interna Passing Juice
16.5. Natural Language Inference: Using Attention Interna Passing Juice
16.6. Fine-Tuning BERT for Sequence-Level and Token-Level Applications Interna Passing Juice
16.7. Natural Language Inference: Fine-Tuning BERT Interna Passing Juice
17. Reinforcement Learning Interna Passing Juice
17.1. Markov Decision Process (MDP) Interna Passing Juice
17.2. Value Iteration Interna Passing Juice
17.3. Q-Learning Interna Passing Juice
18. Gaussian Processes Interna Passing Juice
18.1. Introduction to Gaussian Processes Interna Passing Juice
18.2. Gaussian Process Priors Interna Passing Juice
18.3. Gaussian Process Inference Interna Passing Juice
19. Hyperparameter Optimization Interna Passing Juice
19.1. What Is Hyperparameter Optimization? Interna Passing Juice
19.2. Hyperparameter Optimization API Interna Passing Juice
19.3. Asynchronous Random Search Interna Passing Juice
19.4. Multi-Fidelity Hyperparameter Optimization Interna Passing Juice
19.5. Asynchronous Successive Halving Interna Passing Juice
20. Generative Adversarial Networks Interna Passing Juice
20.1. Generative Adversarial Networks Interna Passing Juice
20.2. Deep Convolutional Generative Adversarial Networks Interna Passing Juice
21. Recommender Systems Interna Passing Juice
21.1. Overview of Recommender Systems Interna Passing Juice
21.2. The MovieLens Dataset Interna Passing Juice
21.3. Matrix Factorization Interna Passing Juice
21.4. AutoRec: Rating Prediction with Autoencoders Interna Passing Juice
21.5. Personalized Ranking for Recommender Systems Interna Passing Juice
21.6. Neural Collaborative Filtering for Personalized Ranking Interna Passing Juice
21.7. Sequence-Aware Recommender Systems Interna Passing Juice
21.8. Feature-Rich Recommender Systems Interna Passing Juice
21.9. Factorization Machines Interna Passing Juice
21.10. Deep Factorization Machines Interna Passing Juice
22. Appendix: Mathematics for Deep Learning Interna Passing Juice
22.1. Geometry and Linear Algebraic Operations Interna Passing Juice
22.2. Eigendecompositions Interna Passing Juice
22.3. Single Variable Calculus Interna Passing Juice
22.4. Multivariable Calculus Interna Passing Juice
22.5. Integral Calculus Interna Passing Juice
22.6. Random Variables Interna Passing Juice
22.7. Maximum Likelihood Interna Passing Juice
22.8. Distributions Interna Passing Juice
22.9. Naive Bayes Interna Passing Juice
22.10. Statistics Interna Passing Juice
22.11. Information Theory Interna Passing Juice
23. Appendix: Tools for Deep Learning Interna Passing Juice
23.1. Using Jupyter Notebooks Interna Passing Juice
23.2. Using Amazon SageMaker Interna Passing Juice
23.3. Using AWS EC2 Instances Interna Passing Juice
23.4. Using Google Colab Interna Passing Juice
23.5. Selecting Servers and GPUs Interna Passing Juice
23.6. Contributing to This Book Interna Passing Juice
23.7. Utility Functions and Classes Interna Passing Juice
References Interna Passing Juice
Interna Passing Juice
Star Externa Passing Juice
Follow @D2L_ai Externa Passing Juice
order Externa Passing Juice
best seller Externa Passing Juice
us Externa Passing Juice
Portuguese Externa Passing Juice
Turkish Externa Passing Juice
Vietnamese Externa Passing Juice
Korean Externa Passing Juice
Japanese Externa Passing Juice
run this book Interna Passing Juice
SageMaker Studio Lab Externa Passing Juice
syllabus page Externa Passing Juice
Aston Zhang Externa Passing Juice
Zack C. Lipton Externa Passing Juice
Mu Li Externa Passing Juice
Alex J. Smola Externa Passing Juice
Pratik Chaudhari Externa Passing Juice
Rasool Fakoor Externa Passing Juice
Kavosh Asadi Externa Passing Juice
Andrew Gordon Wilson Externa Passing Juice
Aaron Klein Externa Passing Juice
Matthias Seeger Externa Passing Juice
Cedric Archambeau Externa Passing Juice
Shuai Zhang Externa Passing Juice
Yi Tay Externa Passing Juice
Brent Werness Externa Passing Juice
Rachel Hu Externa Passing Juice
Anirudh Dagar Externa Passing Juice
Yuan Tang Externa Passing Juice
community contributors Externa Passing Juice
Contribute to the book Externa Passing Juice
community Externa Passing Juice
1.1. A Motivating Example Interna Passing Juice
1.2. Key Components Interna Passing Juice
1.3. Kinds of Machine Learning Problems Interna Passing Juice
1.4. Roots Interna Passing Juice
1.5. The Road to Deep Learning Interna Passing Juice
1.6. Success Stories Interna Passing Juice
1.7. The Essence of Deep Learning Interna Passing Juice
1.8. Summary Interna Passing Juice
1.9. Exercises Interna Passing Juice

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