Dive into Deep Learning

Dive into Deep Learning

Interactive deep learning book with code, math, and discussions

Implemented with NumPy/MXNet, PyTorch, and TensorFlow

Adopted at 140 universities from 35 countries

Announcements

• [Free resource] If you plan to use D2L to teach your class in the 2021 Spring semester, you may apply for free computing resources for your class by 11/22/2020.
• [Oct 2020] We have added PyTorch implementations up to Chapter 11 (Optimization) and TensorFlow implementations up to Chapter 7 (Modern CNNs). To keep track of the latest updates, please follow D2L's open-source project.
• [Apr 2020] We have re-organized Chapter: NLP pretraining and Chapter: NLP applications, and added sections of BERT (model, data, pretraining, fine-tuning, application) and natural language inference (data, model).
• [Jul 2019] The Chinese version is the No. 1 best seller of new books in "Computers and Internet" at the largest Chinese online bookstore.
• [May 2019] Slides, Jupyter notebooks, assignments, and videos of the Berkeley course can be found at the syllabus page.

Authors

Aston Zhang

Amazon Senior Scientist

Zack C. Lipton

Amazon Scientist
CMU Assistant Professor

Mu Li

Amazon Senior Principal Scientist

Alex J. Smola

Amazon VP/Distinguished Scientist

Chapter Authors

Brent Werness

Amazon Scientist
Mathematics for Deep Learning

Rachel Hu

Amazon Scientist
Mathematics for Deep Learning

Shuai Zhang

ETH Zürich Postdoctoral Researcher
Recommender Systems

Yi Tay

Google Scientist
Recommender Systems

Framework Adaptation Authors

Anirudh Dagar

IIT Roorkee Student
PyTorch Adaptation

Yuan Tang

Ant Group Senior Engineer
TensorFlow Adaptation

We thank all the community contributors
for making this open source book better for everyone.

Contribute to the book.

Star Fork

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

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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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Alexandria University
Ateneo de Naga University
Birla Institute of Technology and Science, Hyderabad
Cairo University
Carnegie Mellon University
College of Engineering Pune
Columbia University
Duke University
Durban University of Technology
Emory University
Federal University Lokoja
Fudan University
Gayatri Vidya Parishad College of Engineering (Autonomous)
Gazi Üniversitesi
Georgia Institute of Technology
Golden Gate University
Habib University
Hangzhou Dianzi University
Hankuk University of Foreign Studies
Harbin Institute of Technology
Hasso-Plattner-Institut
Heinrich-Heine-Universität Düsseldorf
Hiroshima University
Ho Chi Minh City University of Foreign Languages and Information Technology
Hochschule Bremen
Hochschule für Technik und Wirtschaft
Hong Kong University of Science and Technology
Huazhong University of Science and Technology
Imperial College London
Indian Institute of Technology Bombay
Indian Institute of Technology Jodhpur
Indian Institute of Technology Kanpur
Indian Institute of Technology Kharagpur
Indian Institute of Technology Mandi
Indian Institute of Technology Ropar
Indira Gandhi National Open University
Indraprastha Institute of Information Technology, Delhi
Institut de recherche en informatique de Toulouse
Institut Supérieur d'Informatique et des Techniques de Communication
Institut Supérieur De L'electronique Et Du Numérique
Instituto Tecnológico Autónomo de México
İstanbul Teknik Üniversitesi
IT-Universitetet i København
King Abdullah University of Science and Technology
Kongu Engineering College
KPR Institute of Engineering and Technology
Kyungpook National University
Lancaster University
Leibniz Universität Hannover
Leuphana University of Lüneburg
London School of Economics & Political Science
Massachusetts Institute of Technology
McGill University
Milwaukee School of Engineering
Minia University
Monash University
Multimedia University
National Chung Hsing University
National Institute of Technical Teachers Training&Research
National Institute of Technology, Warangal
National Taiwan University
National United University
National University of Singapore
Nazarbayev University
New York University
Newman University
North Ossetian State University
Northeastern University
Ohio University
Peking University
Politecnico di Milano
Pontificia Universidad Católica de Chile
Portland State University
Purdue University
Queen's University
Radboud Universiteit
Rowan University
Rutgers, The State University of New Jersey
Sapienza Università di Roma
Shanghai Jiao Tong University
Shanghai University of Finance and Economics
Sogang University
Southern New Hampshire University
St. Pölten University of Applied Sciences
Stanford University
Stevens Institute of Technology
Technische Universiteit Delft
Tekirdağ Namık Kemal Üniversitesi
Texas A&M University
Thapar Institute of Engineering and Technology
The State University of New York at Binghamton
The University of Texas at Austin
Tsinghua University
Universidad Carlos III de Madrid
Universidad de Zaragoza
Universidad Militar Nueva Granada
Universidad Nacional Agraria La Molina
Universidad Nacional de Colombia Sede Manizales
Universidade Federal de Minas Gerais
Universidade Federal de Ouro Preto
Universidade Federal do Rio Grande
Universidade NOVA de Lisboa
Universidade Presbiteriana Mackenzie
Università degli Studi di Brescia
Università degli Studi di Catania
Università degli Studi di Padova
Universität Heidelberg
Universitat Politècnica de Catalunya
Universitatea de Vest din Timișoara
Université Paris-Saclay
University of Arkansas
University of Augsburg
University of California, Berkeley
University of California, Los Angeles
University of California, San Diego
University of California, Santa Barbara
University of Cambridge
University of Cincinnati
University of Illinois at Urbana-Champaign
University of Liège
University of Maryland
University of Minnesota, Twin Cities
University of New Hampshire
University of North Carolina at Chapel Hill
University of North Texas
University of Northern Philippines
University of Pennsylvania
University of Science and Technology of China
University of Southern Maine
University of St Andrews
University of Technology Sydney
University of Washington
University of Waterloo
University of Wisconsin Madison
Univerzita Komenského v Bratislave
Vietnamese-German University
Wageningen University
West Virginia University
Western University
Xavier University Bhubaneswar
Yeshiva University
Yunnan University
Zhejiang University

If you use D2L to teach (or plan to) and would like to receive a free hardcopy, please contact us.

BibTeX entry for citing the book

@book{zhang2020dive,
    title={Dive into Deep Learning},
    author={Aston Zhang and Zachary C. Lipton and Mu Li and Alexander J. Smola},
    note={\url{https://d2l.ai}},
    year={2020}
}

Table of contents

• Preface
• Installation
• Notation

• 1. Introduction
  • 1.1. A Motivating Example
  • 1.2. The Key Components: Data, Models, and Algorithms
  • 1.3. Kinds of Machine Learning
  • 1.4. Roots
  • 1.5. The Road to Deep Learning
  • 1.6. Success Stories
  • 1.7. Summary
  • 1.8. 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
  • 2.7. Documentation
• 3. Linear Neural Networks
  • 3.1. Linear Regression
  • 3.2. Linear Regression Implementation from Scratch
  • 3.3. Concise Implementation of Linear Regression
  • 3.4. Softmax Regression
  • 3.5. The Image Classification Dataset
  • 3.6. Implementation of Softmax Regression from Scratch
  • 3.7. Concise Implementation of Softmax Regression
• 4. Multilayer Perceptrons
  • 4.1. Multilayer Perceptrons
  • 4.2. Implementation of Multilayer Perceptrons from Scratch
  • 4.3. Concise Implementation of Multilayer Perceptrons
  • 4.4. Model Selection, Underfitting, and Overfitting
  • 4.5. Weight Decay
  • 4.6. Dropout
  • 4.7. Forward Propagation, Backward Propagation, and Computational Graphs
  • 4.8. Numerical Stability and Initialization
  • 4.9. Environment and Distribution Shift
  • 4.10. Predicting House Prices on Kaggle
• 5. Deep Learning Computation
  • 5.1. Layers and Blocks
  • 5.2. Parameter Management
  • 5.3. Deferred Initialization
  • 5.4. Custom Layers
  • 5.5. File I/O
  • 5.6. GPUs
• 6. Convolutional Neural Networks
  • 6.1. From Fully-Connected Layers to Convolutions
  • 6.2. Convolutions for Images
  • 6.3. Padding and Stride
  • 6.4. Multiple Input and Multiple Output Channels
  • 6.5. Pooling
  • 6.6. Convolutional Neural Networks (LeNet)
• 7. Modern Convolutional Neural Networks
  • 7.1. Deep Convolutional Neural Networks (AlexNet)
  • 7.2. Networks Using Blocks (VGG)
  • 7.3. Network in Network (NiN)
  • 7.4. Networks with Parallel Concatenations (GoogLeNet)
  • 7.5. Batch Normalization
  • 7.6. Residual Networks (ResNet)
  • 7.7. Densely Connected Networks (DenseNet)
• 8. Recurrent Neural Networks
  • 8.1. Sequence Models
  • 8.2. Text Preprocessing
  • 8.3. Language Models and the Dataset
  • 8.4. Recurrent Neural Networks
  • 8.5. Implementation of Recurrent Neural Networks from Scratch
  • 8.6. Concise Implementation of Recurrent Neural Networks
  • 8.7. Backpropagation Through Time
• 9. Modern Recurrent Neural Networks
  • 9.1. Gated Recurrent Units (GRU)
  • 9.2. Long Short-Term Memory (LSTM)
  • 9.3. Deep Recurrent Neural Networks
  • 9.4. Bidirectional Recurrent Neural Networks
  • 9.5. Machine Translation and the Dataset
  • 9.6. Encoder-Decoder Architecture
  • 9.7. Sequence to Sequence Learning
  • 9.8. Beam Search
• 10. Attention Mechanisms
  • 10.1. Attention Mechanisms
  • 10.2. Sequence to Sequence with Attention Mechanisms
  • 10.3. Transformer
• 11. Optimization Algorithms
  • 11.1. Optimization and Deep Learning
  • 11.2. Convexity
  • 11.3. Gradient Descent
  • 11.4. Stochastic Gradient Descent
  • 11.5. Minibatch Stochastic Gradient Descent
  • 11.6. Momentum
  • 11.7. Adagrad
  • 11.8. RMSProp
  • 11.9. Adadelta
  • 11.10. Adam
  • 11.11. Learning Rate Scheduling
• 12. Computational Performance
  • 12.1. Compilers and Interpreters
  • 12.2. Asynchronous Computation
  • 12.3. Automatic Parallelism
  • 12.4. Hardware
  • 12.5. Training on Multiple GPUs
  • 12.6. Concise Implementation for Multiple GPUs
  • 12.7. Parameter Servers
• 13. Computer Vision
  • 13.1. Image Augmentation
  • 13.2. Fine-Tuning
  • 13.3. Object Detection and Bounding Boxes
  • 13.4. Anchor Boxes
  • 13.5. Multiscale Object Detection
  • 13.6. The Object Detection Dataset
  • 13.7. Single Shot Multibox Detection (SSD)
  • 13.8. Region-based CNNs (R-CNNs)
  • 13.9. Semantic Segmentation and the Dataset
  • 13.10. Transposed Convolution
  • 13.11. Fully Convolutional Networks (FCN)
  • 13.12. Neural Style Transfer
  • 13.13. Image Classification (CIFAR-10) on Kaggle
  • 13.14. Dog Breed Identification (ImageNet Dogs) on Kaggle
• 14. Natural Language Processing: Pretraining
  • 14.1. Word Embedding (word2vec)
  • 14.2. Approximate Training
  • 14.3. The Dataset for Pretraining Word Embedding
  • 14.4. Pretraining word2vec
  • 14.5. Word Embedding with Global Vectors (GloVe)
  • 14.6. Subword Embedding
  • 14.7. Finding Synonyms and Analogies
  • 14.8. Bidirectional Encoder Representations from Transformers (BERT)
  • 14.9. The Dataset for Pretraining BERT
  • 14.10. Pretraining BERT
• 15. Natural Language Processing: Applications
  • 15.1. Sentiment Analysis and the Dataset
  • 15.2. Sentiment Analysis: Using Recurrent Neural Networks
  • 15.3. Sentiment Analysis: Using Convolutional Neural Networks
  • 15.4. Natural Language Inference and the Dataset
  • 15.5. Natural Language Inference: Using Attention
  • 15.6. Fine-Tuning BERT for Sequence-Level and Token-Level Applications
  • 15.7. Natural Language Inference: Fine-Tuning BERT
• 16. Recommender Systems
  • 16.1. Overview of Recommender Systems
  • 16.2. The MovieLens Dataset
  • 16.3. Matrix Factorization
  • 16.4. AutoRec: Rating Prediction with Autoencoders
  • 16.5. Personalized Ranking for Recommender Systems
  • 16.6. Neural Collaborative Filtering for Personalized Ranking
  • 16.7. Sequence-Aware Recommender Systems
  • 16.8. Feature-Rich Recommender Systems
  • 16.9. Factorization Machines
  • 16.10. Deep Factorization Machines
• 17. Generative Adversarial Networks
  • 17.1. Generative Adversarial Networks
  • 17.2. Deep Convolutional Generative Adversarial Networks
• 18. Appendix: Mathematics for Deep Learning
  • 18.1. Geometry and Linear Algebraic Operations
  • 18.2. Eigendecompositions
  • 18.3. Single Variable Calculus
  • 18.4. Multivariable Calculus
  • 18.5. Integral Calculus
  • 18.6. Random Variables
  • 18.7. Maximum Likelihood
  • 18.8. Distributions
  • 18.9. Naive Bayes
  • 18.10. Statistics
  • 18.11. Information Theory
• 19. Appendix: Tools for Deep Learning
  • 19.1. Using Jupyter
  • 19.2. Using Amazon SageMaker
  • 19.3. Using AWS EC2 Instances
  • 19.4. Using Google Colab
  • 19.5. Selecting Servers and GPUs
  • 19.6. Contributing to This Book
  • 19.7. d2l API Document

• References