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Dive into Deep Learning

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Preview Version PyTorch MXNet Notebooks Courses GitHub 中文版
Table Of Contents
 * Preface
 * Installation
 * Notation

 * 1. Introduction
 * 2. Preliminarieskeyboard_arrow_down
   * 2.1. Data Manipulation
   * 2.2. Data Preprocessing
   * 2.3. Linear Algebra
   * 2.4. Calculus
   * 2.5. Automatic Differentiation
   * 2.6. Probability and Statistics
   * 2.7. Documentation
 * 3. Linear Neural Networks for Regressionkeyboard_arrow_down
   * 3.1. Linear Regression
   * 3.2. Object-Oriented Design for Implementation
   * 3.3. Synthetic Regression Data
   * 3.4. Linear Regression Implementation from Scratch
   * 3.5. Concise Implementation of Linear Regression
   * 3.6. Generalization
   * 3.7. Weight Decay
 * 4. Linear Neural Networks for Classificationkeyboard_arrow_down
   * 4.1. Softmax Regression
   * 4.2. The Image Classification Dataset
   * 4.3. The Base Classification Model
   * 4.4. Softmax Regression Implementation from Scratch
   * 4.5. Concise Implementation of Softmax Regression
   * 4.6. Generalization in Classification
   * 4.7. Environment and Distribution Shift
 * 5. Multilayer Perceptronskeyboard_arrow_down
   * 5.1. Multilayer Perceptrons
   * 5.2. Implementation of Multilayer Perceptrons
   * 5.3. Forward Propagation, Backward Propagation, and Computational Graphs
   * 5.4. Numerical Stability and Initialization
   * 5.5. Generalization in Deep Learning
   * 5.6. Dropout
   * 5.7. Predicting House Prices on Kaggle
 * 6. Builders’ Guidekeyboard_arrow_down
   * 6.1. Layers and Modules
   * 6.2. Parameter Management
   * 6.3. Parameter Initialization
   * 6.4. Lazy Initialization
   * 6.5. Custom Layers
   * 6.6. File I/O
   * 6.7. GPUs
 * 7. Convolutional Neural Networkskeyboard_arrow_down
   * 7.1. From Fully Connected Layers to Convolutions
   * 7.2. Convolutions for Images
   * 7.3. Padding and Stride
   * 7.4. Multiple Input and Multiple Output Channels
   * 7.5. Pooling
   * 7.6. Convolutional Neural Networks (LeNet)
 * 8. Modern Convolutional Neural Networkskeyboard_arrow_down
   * 8.1. Deep Convolutional Neural Networks (AlexNet)
   * 8.2. Networks Using Blocks (VGG)
   * 8.3. Network in Network (NiN)
   * 8.4. Multi-Branch Networks (GoogLeNet)
   * 8.5. Batch Normalization
   * 8.6. Residual Networks (ResNet) and ResNeXt
   * 8.7. Densely Connected Networks (DenseNet)
   * 8.8. Designing Convolution Network Architectures
 * 9. Recurrent Neural Networkskeyboard_arrow_down
   * 9.1. Working with Sequences
   * 9.2. Converting Raw Text into Sequence Data
   * 9.3. Language Models
   * 9.4. Recurrent Neural Networks
   * 9.5. Recurrent Neural Network Implementation from Scratch
   * 9.6. Concise Implementation of Recurrent Neural Networks
   * 9.7. Backpropagation Through Time
 * 10. Modern Recurrent Neural Networkskeyboard_arrow_down
   * 10.1. Long Short-Term Memory (LSTM)
   * 10.2. Gated Recurrent Units (GRU)
   * 10.3. Deep Recurrent Neural Networks
   * 10.4. Bidirectional Recurrent Neural Networks
   * 10.5. Machine Translation and the Dataset
   * 10.6. The Encoder–Decoder Architecture
   * 10.7. Sequence-to-Sequence Learning for Machine Translation
   * 10.8. Beam Search
 * 11. Attention Mechanisms and Transformerskeyboard_arrow_down
   * 11.1. Queries, Keys, and Values
   * 11.2. Attention Pooling by Similarity
   * 11.3. Attention Scoring Functions
   * 11.4. The Bahdanau Attention Mechanism
   * 11.5. Multi-Head Attention
   * 11.6. Self-Attention and Positional Encoding
   * 11.7. The Transformer Architecture
   * 11.8. Transformers for Vision
   * 11.9. Large-Scale Pretraining with Transformers
 * 12. Optimization Algorithmskeyboard_arrow_down
   * 12.1. Optimization and Deep Learning
   * 12.2. Convexity
   * 12.3. Gradient Descent
   * 12.4. Stochastic Gradient Descent
   * 12.5. Minibatch Stochastic Gradient Descent
   * 12.6. Momentum
   * 12.7. Adagrad
   * 12.8. RMSProp
   * 12.9. Adadelta
   * 12.10. Adam
   * 12.11. Learning Rate Scheduling
 * 13. Computational Performancekeyboard_arrow_down
   * 13.1. Compilers and Interpreters
   * 13.2. Asynchronous Computation
   * 13.3. Automatic Parallelism
   * 13.4. Hardware
   * 13.5. Training on Multiple GPUs
   * 13.6. Concise Implementation for Multiple GPUs
   * 13.7. Parameter Servers
 * 14. Computer Visionkeyboard_arrow_down
   * 14.1. Image Augmentation
   * 14.2. Fine-Tuning
   * 14.3. Object Detection and Bounding Boxes
   * 14.4. Anchor Boxes
   * 14.5. Multiscale Object Detection
   * 14.6. The Object Detection Dataset
   * 14.7. Single Shot Multibox Detection
   * 14.8. Region-based CNNs (R-CNNs)
   * 14.9. Semantic Segmentation and the Dataset
   * 14.10. Transposed Convolution
   * 14.11. Fully Convolutional Networks
   * 14.12. Neural Style Transfer
   * 14.13. Image Classification (CIFAR-10) on Kaggle
   * 14.14. Dog Breed Identification (ImageNet Dogs) on Kaggle
 * 15. Natural Language Processing: Pretrainingkeyboard_arrow_down
   * 15.1. Word Embedding (word2vec)
   * 15.2. Approximate Training
   * 15.3. The Dataset for Pretraining Word Embeddings
   * 15.4. Pretraining word2vec
   * 15.5. Word Embedding with Global Vectors (GloVe)
   * 15.6. Subword Embedding
   * 15.7. Word Similarity and Analogy
   * 15.8. Bidirectional Encoder Representations from Transformers (BERT)
   * 15.9. The Dataset for Pretraining BERT
   * 15.10. Pretraining BERT
 * 16. Natural Language Processing: Applicationskeyboard_arrow_down
   * 16.1. Sentiment Analysis and the Dataset
   * 16.2. Sentiment Analysis: Using Recurrent Neural Networks
   * 16.3. Sentiment Analysis: Using Convolutional Neural Networks
   * 16.4. Natural Language Inference and the Dataset
   * 16.5. Natural Language Inference: Using Attention
   * 16.6. Fine-Tuning BERT for Sequence-Level and Token-Level Applications
   * 16.7. Natural Language Inference: Fine-Tuning BERT
 * 17. Reinforcement Learningkeyboard_arrow_down
   * 17.1. Markov Decision Process (MDP)
   * 17.2. Value Iteration
   * 17.3. Q-Learning
 * 18. Gaussian Processeskeyboard_arrow_down
   * 18.1. Introduction to Gaussian Processes
   * 18.2. Gaussian Process Priors
   * 18.3. Gaussian Process Inference
 * 19. Hyperparameter Optimizationkeyboard_arrow_down
   * 19.1. What Is Hyperparameter Optimization?
   * 19.2. Hyperparameter Optimization API
   * 19.3. Asynchronous Random Search
   * 19.4. Multi-Fidelity Hyperparameter Optimization
   * 19.5. Asynchronous Successive Halving
 * 20. Generative Adversarial Networkskeyboard_arrow_down
   * 20.1. Generative Adversarial Networks
   * 20.2. Deep Convolutional Generative Adversarial Networks
 * 21. Recommender Systemskeyboard_arrow_down
   * 21.1. Overview of Recommender Systems
   * 21.2. The MovieLens Dataset
   * 21.3. Matrix Factorization
   * 21.4. AutoRec: Rating Prediction with Autoencoders
   * 21.5. Personalized Ranking for Recommender Systems
   * 21.6. Neural Collaborative Filtering for Personalized Ranking
   * 21.7. Sequence-Aware Recommender Systems
   * 21.8. Feature-Rich Recommender Systems
   * 21.9. Factorization Machines
   * 21.10. Deep Factorization Machines
 * 22. Appendix: Mathematics for Deep Learningkeyboard_arrow_down
   * 22.1. Geometry and Linear Algebraic Operations
   * 22.2. Eigendecompositions
   * 22.3. Single Variable Calculus
   * 22.4. Multivariable Calculus
   * 22.5. Integral Calculus
   * 22.6. Random Variables
   * 22.7. Maximum Likelihood
   * 22.8. Distributions
   * 22.9. Naive Bayes
   * 22.10. Statistics
   * 22.11. Information Theory
 * 23. Appendix: Tools for Deep Learningkeyboard_arrow_down
   * 23.1. Using Jupyter Notebooks
   * 23.2. Using Amazon SageMaker
   * 23.3. Using AWS EC2 Instances
   * 23.4. Using Google Colab
   * 23.5. Selecting Servers and GPUs
   * 23.6. Contributing to This Book
   * 23.7. Utility Functions and Classes
   * 23.8. The d2l API Document

 * References

Table Of Contents
 * Preface
 * Installation
 * Notation

 * 1. Introduction
 * 2. Preliminarieskeyboard_arrow_down
   * 2.1. Data Manipulation
   * 2.2. Data Preprocessing
   * 2.3. Linear Algebra
   * 2.4. Calculus
   * 2.5. Automatic Differentiation
   * 2.6. Probability and Statistics
   * 2.7. Documentation
 * 3. Linear Neural Networks for Regressionkeyboard_arrow_down
   * 3.1. Linear Regression
   * 3.2. Object-Oriented Design for Implementation
   * 3.3. Synthetic Regression Data
   * 3.4. Linear Regression Implementation from Scratch
   * 3.5. Concise Implementation of Linear Regression
   * 3.6. Generalization
   * 3.7. Weight Decay
 * 4. Linear Neural Networks for Classificationkeyboard_arrow_down
   * 4.1. Softmax Regression
   * 4.2. The Image Classification Dataset
   * 4.3. The Base Classification Model
   * 4.4. Softmax Regression Implementation from Scratch
   * 4.5. Concise Implementation of Softmax Regression
   * 4.6. Generalization in Classification
   * 4.7. Environment and Distribution Shift
 * 5. Multilayer Perceptronskeyboard_arrow_down
   * 5.1. Multilayer Perceptrons
   * 5.2. Implementation of Multilayer Perceptrons
   * 5.3. Forward Propagation, Backward Propagation, and Computational Graphs
   * 5.4. Numerical Stability and Initialization
   * 5.5. Generalization in Deep Learning
   * 5.6. Dropout
   * 5.7. Predicting House Prices on Kaggle
 * 6. Builders’ Guidekeyboard_arrow_down
   * 6.1. Layers and Modules
   * 6.2. Parameter Management
   * 6.3. Parameter Initialization
   * 6.4. Lazy Initialization
   * 6.5. Custom Layers
   * 6.6. File I/O
   * 6.7. GPUs
 * 7. Convolutional Neural Networkskeyboard_arrow_down
   * 7.1. From Fully Connected Layers to Convolutions
   * 7.2. Convolutions for Images
   * 7.3. Padding and Stride
   * 7.4. Multiple Input and Multiple Output Channels
   * 7.5. Pooling
   * 7.6. Convolutional Neural Networks (LeNet)
 * 8. Modern Convolutional Neural Networkskeyboard_arrow_down
   * 8.1. Deep Convolutional Neural Networks (AlexNet)
   * 8.2. Networks Using Blocks (VGG)
   * 8.3. Network in Network (NiN)
   * 8.4. Multi-Branch Networks (GoogLeNet)
   * 8.5. Batch Normalization
   * 8.6. Residual Networks (ResNet) and ResNeXt
   * 8.7. Densely Connected Networks (DenseNet)
   * 8.8. Designing Convolution Network Architectures
 * 9. Recurrent Neural Networkskeyboard_arrow_down
   * 9.1. Working with Sequences
   * 9.2. Converting Raw Text into Sequence Data
   * 9.3. Language Models
   * 9.4. Recurrent Neural Networks
   * 9.5. Recurrent Neural Network Implementation from Scratch
   * 9.6. Concise Implementation of Recurrent Neural Networks
   * 9.7. Backpropagation Through Time
 * 10. Modern Recurrent Neural Networkskeyboard_arrow_down
   * 10.1. Long Short-Term Memory (LSTM)
   * 10.2. Gated Recurrent Units (GRU)
   * 10.3. Deep Recurrent Neural Networks
   * 10.4. Bidirectional Recurrent Neural Networks
   * 10.5. Machine Translation and the Dataset
   * 10.6. The Encoder–Decoder Architecture
   * 10.7. Sequence-to-Sequence Learning for Machine Translation
   * 10.8. Beam Search
 * 11. Attention Mechanisms and Transformerskeyboard_arrow_down
   * 11.1. Queries, Keys, and Values
   * 11.2. Attention Pooling by Similarity
   * 11.3. Attention Scoring Functions
   * 11.4. The Bahdanau Attention Mechanism
   * 11.5. Multi-Head Attention
   * 11.6. Self-Attention and Positional Encoding
   * 11.7. The Transformer Architecture
   * 11.8. Transformers for Vision
   * 11.9. Large-Scale Pretraining with Transformers
 * 12. Optimization Algorithmskeyboard_arrow_down
   * 12.1. Optimization and Deep Learning
   * 12.2. Convexity
   * 12.3. Gradient Descent
   * 12.4. Stochastic Gradient Descent
   * 12.5. Minibatch Stochastic Gradient Descent
   * 12.6. Momentum
   * 12.7. Adagrad
   * 12.8. RMSProp
   * 12.9. Adadelta
   * 12.10. Adam
   * 12.11. Learning Rate Scheduling
 * 13. Computational Performancekeyboard_arrow_down
   * 13.1. Compilers and Interpreters
   * 13.2. Asynchronous Computation
   * 13.3. Automatic Parallelism
   * 13.4. Hardware
   * 13.5. Training on Multiple GPUs
   * 13.6. Concise Implementation for Multiple GPUs
   * 13.7. Parameter Servers
 * 14. Computer Visionkeyboard_arrow_down
   * 14.1. Image Augmentation
   * 14.2. Fine-Tuning
   * 14.3. Object Detection and Bounding Boxes
   * 14.4. Anchor Boxes
   * 14.5. Multiscale Object Detection
   * 14.6. The Object Detection Dataset
   * 14.7. Single Shot Multibox Detection
   * 14.8. Region-based CNNs (R-CNNs)
   * 14.9. Semantic Segmentation and the Dataset
   * 14.10. Transposed Convolution
   * 14.11. Fully Convolutional Networks
   * 14.12. Neural Style Transfer
   * 14.13. Image Classification (CIFAR-10) on Kaggle
   * 14.14. Dog Breed Identification (ImageNet Dogs) on Kaggle
 * 15. Natural Language Processing: Pretrainingkeyboard_arrow_down
   * 15.1. Word Embedding (word2vec)
   * 15.2. Approximate Training
   * 15.3. The Dataset for Pretraining Word Embeddings
   * 15.4. Pretraining word2vec
   * 15.5. Word Embedding with Global Vectors (GloVe)
   * 15.6. Subword Embedding
   * 15.7. Word Similarity and Analogy
   * 15.8. Bidirectional Encoder Representations from Transformers (BERT)
   * 15.9. The Dataset for Pretraining BERT
   * 15.10. Pretraining BERT
 * 16. Natural Language Processing: Applicationskeyboard_arrow_down
   * 16.1. Sentiment Analysis and the Dataset
   * 16.2. Sentiment Analysis: Using Recurrent Neural Networks
   * 16.3. Sentiment Analysis: Using Convolutional Neural Networks
   * 16.4. Natural Language Inference and the Dataset
   * 16.5. Natural Language Inference: Using Attention
   * 16.6. Fine-Tuning BERT for Sequence-Level and Token-Level Applications
   * 16.7. Natural Language Inference: Fine-Tuning BERT
 * 17. Reinforcement Learningkeyboard_arrow_down
   * 17.1. Markov Decision Process (MDP)
   * 17.2. Value Iteration
   * 17.3. Q-Learning
 * 18. Gaussian Processeskeyboard_arrow_down
   * 18.1. Introduction to Gaussian Processes
   * 18.2. Gaussian Process Priors
   * 18.3. Gaussian Process Inference
 * 19. Hyperparameter Optimizationkeyboard_arrow_down
   * 19.1. What Is Hyperparameter Optimization?
   * 19.2. Hyperparameter Optimization API
   * 19.3. Asynchronous Random Search
   * 19.4. Multi-Fidelity Hyperparameter Optimization
   * 19.5. Asynchronous Successive Halving
 * 20. Generative Adversarial Networkskeyboard_arrow_down
   * 20.1. Generative Adversarial Networks
   * 20.2. Deep Convolutional Generative Adversarial Networks
 * 21. Recommender Systemskeyboard_arrow_down
   * 21.1. Overview of Recommender Systems
   * 21.2. The MovieLens Dataset
   * 21.3. Matrix Factorization
   * 21.4. AutoRec: Rating Prediction with Autoencoders
   * 21.5. Personalized Ranking for Recommender Systems
   * 21.6. Neural Collaborative Filtering for Personalized Ranking
   * 21.7. Sequence-Aware Recommender Systems
   * 21.8. Feature-Rich Recommender Systems
   * 21.9. Factorization Machines
   * 21.10. Deep Factorization Machines
 * 22. Appendix: Mathematics for Deep Learningkeyboard_arrow_down
   * 22.1. Geometry and Linear Algebraic Operations
   * 22.2. Eigendecompositions
   * 22.3. Single Variable Calculus
   * 22.4. Multivariable Calculus
   * 22.5. Integral Calculus
   * 22.6. Random Variables
   * 22.7. Maximum Likelihood
   * 22.8. Distributions
   * 22.9. Naive Bayes
   * 22.10. Statistics
   * 22.11. Information Theory
 * 23. Appendix: Tools for Deep Learningkeyboard_arrow_down
   * 23.1. Using Jupyter Notebooks
   * 23.2. Using Amazon SageMaker
   * 23.3. Using AWS EC2 Instances
   * 23.4. Using Google Colab
   * 23.5. Selecting Servers and GPUs
   * 23.6. Contributing to This Book
   * 23.7. Utility Functions and Classes
   * 23.8. The d2l API Document

 * References


DIVE INTO DEEP LEARNING¶


DIVE INTO DEEP LEARNING

Interactive deep learning book with code, math, and discussions

Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow

Adopted at 500 universities from 70 countries



 * [Feb 2023] The book is forthcoming on Cambridge University Press (order). The
   Chinese version is the best seller at the largest Chinese online bookstore.
   Follow D2L's open-source project for the latest updates.
 * [Dec 2022] JAX implementation is available! New topics of reinforcement
   learning, Gaussian processes, and hyperparameter optimization are added!
 * [Jul 2022] Check out our new API for implementation and new topics like
   generalization in classification and deep learning, ResNeXt, CNN design
   space, and transformers for vision and large-scale pretraining.
 * [May 2022] Join us to improve ongoing translations in Portuguese, Turkish,
   Vietnamese, Korean, and Japanese.
 * [Dec 2021] We added a new option to run this book for free: check out
   SageMaker Studio Lab.
 * [May 2021] Slides, Jupyter notebooks, assignments, and videos of the Berkeley
   course can be found at the syllabus page.


AUTHORS


ASTON ZHANG

Amazon


ZACK C. LIPTON

CMU and Amazon


MU LI

Amazon


ALEX J. SMOLA

Amazon


VOL.2 CHAPTER AUTHORS


PRATIK CHAUDHARI

UPenn and Amazon
Reinforcement Learning


RASOOL FAKOOR

Amazon
Reinforcement Learning


KAVOSH ASADI

Amazon
Reinforcement Learning


ANDREW GORDON WILSON

NYU and Amazon
Gaussian Processes


AARON KLEIN

Amazon
Hyperparameter Optimization


MATTHIAS SEEGER

Amazon
Hyperparameter Optimization


CEDRIC ARCHAMBEAU

Amazon
Hyperparameter Optimization


SHUAI ZHANG

Amazon
Recommender Systems


YI TAY

Google
Recommender Systems


BRENT WERNESS

Amazon
Mathematics for Deep Learning


RACHEL HU

Amazon
Mathematics for Deep Learning


FRAMEWORK ADAPTATION AUTHORS


ANIRUDH DAGAR

Amazon
PyTorch Adaptation
JAX Adaptation


YUAN TANG

Akuity
TensorFlow Adaptation


WE THANK ALL THE COMMUNITY CONTRIBUTORS
FOR MAKING THIS OPEN SOURCE BOOK BETTER FOR EVERYONE.

CONTRIBUTE TO THE BOOK


EACH SECTION IS AN EXECUTABLE JUPYTER NOTEBOOK

You can modify the code and tune hyperparameters to get instant feedback to
accumulate practical experiences in deep learning.

Run
locally

Amazon SageMaker
Studio Lab

Amazon
SageMaker

Google
Colab




MATHEMATICS + FIGURES + CODE

We offer an interactive learning experience with mathematics, figures, code,
text, and discussions, where concepts and techniques are illustrated and
implemented with experiments on real data sets.






ACTIVE COMMUNITY SUPPORT

You can discuss and learn with thousands of peers in the community through the
link provided in each section.


D2L AS A TEXTBOOK OR A REFERENCE BOOK









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Abasyn University, Islamabad Campus
Alexandria University
Amirkabir University of Technology
Amity University
Amrita Vishwa Vidyapeetham University
Anna University
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Ateneo de Naga University
Australian National University
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Quaid-e-Azam University
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Rajiv Gandhi Institute of Petroleum Technology
Rensselaer Polytechnic Institute
Rowan University
Rutgers, The State University of New Jersey
RVS Institute of Management Studies and Research
RWTH Aachen University
Sant Longowal Institute of Engineering Technology
Santa Clara University
Sapienza Università di Roma
Seoul National University
Seoul National University of Science and Technology
Shanghai Jiao Tong University
Shanghai University of Electric Power
Shanghai University of Finance and Economics
Shantilal Shah Engineering College
Sharif University of Technology
Shenzhen University
Shivaji University, Kolhapur
Simon Fraser University
Singapore University of Technology and Design
Sogang University
Sookmyung Women's University
Southern Connecticut State University
Southern New Hampshire University
St. Pölten University of Applied Sciences
Stanford University
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Sungkyunkwan University
Technion - Israel Institute of Technology
Technische Universität Berlin
Technische Universität München
Technische Universiteit Delft
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Tekirdağ Namık Kemal Üniversitesi
Télécom Paris
Telkom University
Texas A&M University
Thapar Institute of Engineering and Technology
Tsinghua University
Tufts University
Umeå University
Universidad Carlos III de Madrid
Universidad de Ibagué
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Universidad de Salamanca
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Université Clermont Auvergne
Université Côte d'Azur
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University of Baghdad
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University of Udine
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Univerzita Komenského v Bratislave
Uniwersytet Jagielloński
Vardhaman College of Engineering
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Vietnamese-German University
Vignana Jyothi Institute Of Management
Vilnius University
Wageningen University
West Virginia University
Western University
Wichita State University
Xavier University Bhubaneswar
Xi'an Jiaotong Liverpool University
Xiamen University
Xianning Vocational Technical College
Yale University
Yeshiva University
Yıldız Teknik Üniversitesi
Yonsei University
Yunnan University
Zhejiang University


BIBTEX ENTRY FOR CITING THE BOOK


@book{zhang2023dive,
    title={Dive into Deep Learning},
    author={Zhang, Aston and Lipton, Zachary C. and Li, Mu and Smola, Alexander J.},
    publisher={Cambridge University Press},
    note={\url{https://D2L.ai}},
    year={2023}
}


TABLE OF CONTENTS

 * Preface
 * Installation
 * Notation

 * 1. Introduction
   * 1.1. A Motivating Example
   * 1.2. Key Components
   * 1.3. Kinds of Machine Learning Problems
   * 1.4. Roots
   * 1.5. The Road to Deep Learning
   * 1.6. Success Stories
   * 1.7. The Essence of Deep Learning
   * 1.8. Summary
   * 1.9. Exercises
 * 2. Preliminaries
   * 2.1. Data Manipulation
   * 2.2. Data Preprocessing
   * 2.3. Linear Algebra
   * 2.4. Calculus
   * 2.5. Automatic Differentiation
   * 2.6. Probability and Statistics
   * 2.7. Documentation
 * 3. Linear Neural Networks for Regression
   * 3.1. Linear Regression
   * 3.2. Object-Oriented Design for Implementation
   * 3.3. Synthetic Regression Data
   * 3.4. Linear Regression Implementation from Scratch
   * 3.5. Concise Implementation of Linear Regression
   * 3.6. Generalization
   * 3.7. Weight Decay
 * 4. Linear Neural Networks for Classification
   * 4.1. Softmax Regression
   * 4.2. The Image Classification Dataset
   * 4.3. The Base Classification Model
   * 4.4. Softmax Regression Implementation from Scratch
   * 4.5. Concise Implementation of Softmax Regression
   * 4.6. Generalization in Classification
   * 4.7. Environment and Distribution Shift
 * 5. Multilayer Perceptrons
   * 5.1. Multilayer Perceptrons
   * 5.2. Implementation of Multilayer Perceptrons
   * 5.3. Forward Propagation, Backward Propagation, and Computational Graphs
   * 5.4. Numerical Stability and Initialization
   * 5.5. Generalization in Deep Learning
   * 5.6. Dropout
   * 5.7. Predicting House Prices on Kaggle
 * 6. Builders’ Guide
   * 6.1. Layers and Modules
   * 6.2. Parameter Management
   * 6.3. Parameter Initialization
   * 6.4. Lazy Initialization
   * 6.5. Custom Layers
   * 6.6. File I/O
   * 6.7. GPUs
 * 7. Convolutional Neural Networks
   * 7.1. From Fully Connected Layers to Convolutions
   * 7.2. Convolutions for Images
   * 7.3. Padding and Stride
   * 7.4. Multiple Input and Multiple Output Channels
   * 7.5. Pooling
   * 7.6. Convolutional Neural Networks (LeNet)
 * 8. Modern Convolutional Neural Networks
   * 8.1. Deep Convolutional Neural Networks (AlexNet)
   * 8.2. Networks Using Blocks (VGG)
   * 8.3. Network in Network (NiN)
   * 8.4. Multi-Branch Networks (GoogLeNet)
   * 8.5. Batch Normalization
   * 8.6. Residual Networks (ResNet) and ResNeXt
   * 8.7. Densely Connected Networks (DenseNet)
   * 8.8. Designing Convolution Network Architectures
 * 9. Recurrent Neural Networks
   * 9.1. Working with Sequences
   * 9.2. Converting Raw Text into Sequence Data
   * 9.3. Language Models
   * 9.4. Recurrent Neural Networks
   * 9.5. Recurrent Neural Network Implementation from Scratch
   * 9.6. Concise Implementation of Recurrent Neural Networks
   * 9.7. Backpropagation Through Time
 * 10. Modern Recurrent Neural Networks
   * 10.1. Long Short-Term Memory (LSTM)
   * 10.2. Gated Recurrent Units (GRU)
   * 10.3. Deep Recurrent Neural Networks
   * 10.4. Bidirectional Recurrent Neural Networks
   * 10.5. Machine Translation and the Dataset
   * 10.6. The Encoder–Decoder Architecture
   * 10.7. Sequence-to-Sequence Learning for Machine Translation
   * 10.8. Beam Search
 * 11. Attention Mechanisms and Transformers
   * 11.1. Queries, Keys, and Values
   * 11.2. Attention Pooling by Similarity
   * 11.3. Attention Scoring Functions
   * 11.4. The Bahdanau Attention Mechanism
   * 11.5. Multi-Head Attention
   * 11.6. Self-Attention and Positional Encoding
   * 11.7. The Transformer Architecture
   * 11.8. Transformers for Vision
   * 11.9. Large-Scale Pretraining with Transformers
 * 12. Optimization Algorithms
   * 12.1. Optimization and Deep Learning
   * 12.2. Convexity
   * 12.3. Gradient Descent
   * 12.4. Stochastic Gradient Descent
   * 12.5. Minibatch Stochastic Gradient Descent
   * 12.6. Momentum
   * 12.7. Adagrad
   * 12.8. RMSProp
   * 12.9. Adadelta
   * 12.10. Adam
   * 12.11. Learning Rate Scheduling
 * 13. Computational Performance
   * 13.1. Compilers and Interpreters
   * 13.2. Asynchronous Computation
   * 13.3. Automatic Parallelism
   * 13.4. Hardware
   * 13.5. Training on Multiple GPUs
   * 13.6. Concise Implementation for Multiple GPUs
   * 13.7. Parameter Servers
 * 14. Computer Vision
   * 14.1. Image Augmentation
   * 14.2. Fine-Tuning
   * 14.3. Object Detection and Bounding Boxes
   * 14.4. Anchor Boxes
   * 14.5. Multiscale Object Detection
   * 14.6. The Object Detection Dataset
   * 14.7. Single Shot Multibox Detection
   * 14.8. Region-based CNNs (R-CNNs)
   * 14.9. Semantic Segmentation and the Dataset
   * 14.10. Transposed Convolution
   * 14.11. Fully Convolutional Networks
   * 14.12. Neural Style Transfer
   * 14.13. Image Classification (CIFAR-10) on Kaggle
   * 14.14. Dog Breed Identification (ImageNet Dogs) on Kaggle
 * 15. Natural Language Processing: Pretraining
   * 15.1. Word Embedding (word2vec)
   * 15.2. Approximate Training
   * 15.3. The Dataset for Pretraining Word Embeddings
   * 15.4. Pretraining word2vec
   * 15.5. Word Embedding with Global Vectors (GloVe)
   * 15.6. Subword Embedding
   * 15.7. Word Similarity and Analogy
   * 15.8. Bidirectional Encoder Representations from Transformers (BERT)
   * 15.9. The Dataset for Pretraining BERT
   * 15.10. Pretraining BERT
 * 16. Natural Language Processing: Applications
   * 16.1. Sentiment Analysis and the Dataset
   * 16.2. Sentiment Analysis: Using Recurrent Neural Networks
   * 16.3. Sentiment Analysis: Using Convolutional Neural Networks
   * 16.4. Natural Language Inference and the Dataset
   * 16.5. Natural Language Inference: Using Attention
   * 16.6. Fine-Tuning BERT for Sequence-Level and Token-Level Applications
   * 16.7. Natural Language Inference: Fine-Tuning BERT
 * 17. Reinforcement Learning
   * 17.1. Markov Decision Process (MDP)
   * 17.2. Value Iteration
   * 17.3. Q-Learning
 * 18. Gaussian Processes
   * 18.1. Introduction to Gaussian Processes
   * 18.2. Gaussian Process Priors
   * 18.3. Gaussian Process Inference
 * 19. Hyperparameter Optimization
   * 19.1. What Is Hyperparameter Optimization?
   * 19.2. Hyperparameter Optimization API
   * 19.3. Asynchronous Random Search
   * 19.4. Multi-Fidelity Hyperparameter Optimization
   * 19.5. Asynchronous Successive Halving
 * 20. Generative Adversarial Networks
   * 20.1. Generative Adversarial Networks
   * 20.2. Deep Convolutional Generative Adversarial Networks
 * 21. Recommender Systems
   * 21.1. Overview of Recommender Systems
   * 21.2. The MovieLens Dataset
   * 21.3. Matrix Factorization
   * 21.4. AutoRec: Rating Prediction with Autoencoders
   * 21.5. Personalized Ranking for Recommender Systems
   * 21.6. Neural Collaborative Filtering for Personalized Ranking
   * 21.7. Sequence-Aware Recommender Systems
   * 21.8. Feature-Rich Recommender Systems
   * 21.9. Factorization Machines
   * 21.10. Deep Factorization Machines
 * 22. Appendix: Mathematics for Deep Learning
   * 22.1. Geometry and Linear Algebraic Operations
   * 22.2. Eigendecompositions
   * 22.3. Single Variable Calculus
   * 22.4. Multivariable Calculus
   * 22.5. Integral Calculus
   * 22.6. Random Variables
   * 22.7. Maximum Likelihood
   * 22.8. Distributions
   * 22.9. Naive Bayes
   * 22.10. Statistics
   * 22.11. Information Theory
 * 23. Appendix: Tools for Deep Learning
   * 23.1. Using Jupyter Notebooks
   * 23.2. Using Amazon SageMaker
   * 23.3. Using AWS EC2 Instances
   * 23.4. Using Google Colab
   * 23.5. Selecting Servers and GPUs
   * 23.6. Contributing to This Book
   * 23.7. Utility Functions and Classes
   * 23.8. The d2l API Document

 * References



Next
Preface