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

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