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Dive into Deep Learning — Dive into Deep Learning 1.0.3 documentation

 Gegenereerd op December 22 2025 11:50 AM

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

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