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

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