Dive into Deep Learning

Dive into Deep Learning

With Classic API

Switch to New API

Interactive deep learning book with code, math, and discussions

Implemented with NumPy/MXNet, PyTorch, and TensorFlow

Adopted at 300 universities from 55 countries

Star

Announcements

• [Dec 2021] We added a new option to run this book for free: check out SageMaker Studio Lab.
• [Jul 2021] We have improved the content and added TensorFlow implementations up to Chapter 11. To keep track of the latest updates, just follow D2L's open-source project.
• [Jan 2021] Check out the brand-new Chapter: Attention Mechanisms. We have also added PyTorch implementations.
• [Apr 2020] We have revamped Chapter: NLP pretraining and Chapter: NLP applications, and added sections of BERT and natural language inference.
• [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

Vol.2 Chapter Authors

Brent Werness

Amazon Senior 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

Akuity Founding Engineer
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

[+] Click here to show the full list.

Abasyn University, Islamabad Campus
Alexandria University
Amirkabir University of Technology
Amity University
Anna University
Ateneo de Naga University
Australian National University
Bar-Ilan University
Barnard College
Birla Institute of Technology and Science, Hyderabad
Birla Institute of Technology and Science, Pilani
Boston University
Brandeis University
Brown University
Brunel University London
Cairo University
California State University, Northridge
Carnegie Mellon University
Chalmers University of Technology
Chennai Mathematical Institute
Chouaib Doukkali University
City College of New York
College of Engineering Pune
Columbia University
Cyprus Institute
Dresden University of Technology
Duke University
Durban University of Technology
Eastern Mediterranean University
Ecole Nationale Supérieure d'Informatique
Eindhoven University of Technology
Emory University
Eötvös Loránd University
Escuela Politécnica Nacional
Escuela Superior Politecnica del Litoral
Federal University Lokoja
Fisk University
Florida Atlantic University
FPT University
Fudan University
Gayatri Vidya Parishad College of Engineering (Autonomous)
Gazi Üniversitesi
George Mason University
Georgetown University
Georgia Institute of Technology
Gheorghe Asachi Technical University of Iaşi
Golden Gate University
Great Lakes Institute of Management
Gwangju Institute of Science and Technology
Habib University
Hamad Bin Khalifa University
Hangzhou Dianzi University
Hangzhou Dianzi University
Hankuk University of Foreign Studies
Harbin Institute of Technology
Harvard University
Hasso-Plattner-Institut
Heinrich-Heine-Universität Düsseldorf
Henan Institute of Technology
Hertie School
Higher Institute of Applied Science and Technology of Sousse
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
Icahn School of Medicine at Mount Sinai
Imperial College London
IMT Mines Alès
Indian Institute of Technology Bombay
Indian Institute of Technology Hyderabad
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
Indian School of Business
Indira Gandhi National Open University
Indraprastha Institute of Information Technology, Delhi
Institut catholique d'arts et métiers (ICAM)
Institut de recherche en informatique de Toulouse
Institut Supérieur d'Informatique et des Techniques de Communication
Institut Supérieur De L'electronique Et Du Numérique
Institut Teknologi Bandung
Instituto Tecnológico Autónomo de México
Instituto Tecnológico de Buenos Aires
Islamic University of Medina
İstanbul Teknik Üniversitesi
IT-Universitetet i København
Jeonbuk National Univerity
Johns Hopkins University
Keio University
King Abdullah University of Science and Technology
King Fahd University of Petroleum and Minerals
Kongu Engineering College
KPR Institute of Engineering and Technology
Kyungpook National University
Lancaster University
Leading Unviersity
Leibniz Universität Hannover
Leuphana University of Lüneburg
London School of Economics & Political Science
M.S.Ramaiah University of Applied Sciences
Make School
Masaryk University
Massachusetts Institute of Technology
McGill University
Menoufia University
Milwaukee School of Engineering
Minia University
Mohammad Ali Jinnah University
Mohammed V University in Rabat
Monash University
Multimedia University
Murdoch University
National Chung Hsing University
National Institute of Technical Teachers Training & Research
National Institute of Technology, Warangal
National Sun Yat-sen University
National Taiwan University
National Technical University of Athens
National Technical University of Ukraine
National United University
National University of Sciences and Technology
National University of Singapore
Nazarbayev University
New Jersey Institute of Technology
New Mexico Institute of Mining and Technology
New York University
Newman University
North Ossetian State University
Northeastern University
Northwestern Polytechnical University
Northwestern University
Ohio University
Pakuan University
Peking University
Pennsylvania State University
Pohang University of Science and Technology
Politecnico di Milano
Politeknik Negeri Semarang
Pomona College
Pontificia Universidad Católica de Chile
Pontificia Universidad Católica del Perú
Portland State University
Purdue University
Quaid-e-Azam University
Queen's University
Radboud Universiteit
Rowan University
Rutgers, The State University of New Jersey
RVS Institute of Management Studies and Research
Santa Clara University
Sapienza Università di Roma
Seoul National University of Science and Technology
Seoul National University
Shanghai Jiao Tong University
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
Southern New Hampshire University
St. Pölten University of Applied Sciences
Stanford University
State University of New York at Binghamton
State University of New York at Fredonia
Stevens Institute of Technology
Sungkyunkwan University
Technion - Israel Institute of Technology
Technische Universität Berlin
Technische Universiteit Delft
Tekirdağ Namık Kemal Üniversitesi
Texas A&M University
Thapar Institute of Engineering and Technology
Tsinghua University
Tufts University
Umeå University
Universidad Carlos III de Madrid
Universidad de Ibagué
Universidad de Ingeniería y Tecnología - UTEC
Universidad de Zaragoza
Universidad Icesi
Universidad Militar Nueva Granada
Universidad Nacional Agraria La Molina
Universidad Nacional Autónoma de México
Universidad Nacional de Colombia Sede Manizales
Universidad Nacional de Tierra del Fuego
Universidad Politécnica de Chiapas
Universidad San Francisco de Quito
Universidad Tecnológica de Pereira
Universidade Católica de Brasília
Universidade Estadual de Campinas
Universidade Federal de Goiás
Universidade Federal de Minas Gerais
Universidade Federal de Ouro Preto
Universidade Federal de São Carlos
Universidade Federal de Viçosa
Universidade Federal do Rio Grande
Universidade NOVA de Lisboa
Universidade Presbiteriana Mackenzie
Università Cattolica del Sacro Cuore
Università degli Studi di Bari Aldo Moro
Università degli Studi di Brescia
Università degli Studi di Catania
Università degli Studi di Padova
Universitas Andalas, Padang
Universitas Indonesia
Universitas Negeri Yogyakarta
Universitas Udayana
Universität Bremen
Universitat de Barcelona
Universität Heidelberg
Universität Leipzig
Universitat Politècnica de Catalunya
Universitatea Babeș-Bolyai
Universitatea de Vest din Timișoara
Université Clermont Auvergne
Université Côte d'Azur
Université de Caen Normandie
Université de Rouen Normandie
Université de technologie de Compiègne
Université Paris-Saclay
Université Toulouse 1 Capitole
University of Arkansas
University of Augsburg
University of Bath
University of British Columbia
University of California, Berkeley
University of California, Irvine
University of California, Los Angeles
University of California, San Diego
University of California, Santa Barbara
University of California, Santa Cruz
University of Cambridge
University of Canberra
University of Cincinnati
University of Connecticut
University of Copenhagen
University of Florida
University of Groningen
University of Hamburg
University of Houston
University of Hull
University of Iceland
University of Idaho
University of Illinois at Urbana-Champaign
University of International Business and Economics
University of Klagenfurt
University of Liège
University of Maryland
University of Massachusetts Lowell
University of Michigan
University of Milano-Bicocca
University of Minnesota, Twin Cities
University of Moratuwa
University of New Hampshire
University of Newcastle
University of North Carolina at Chapel Hill
University of North Texas
University of Northern Philippines
University of Nottingham
University of Pennsylvania
University of Pittsburgh
University of São Paulo
University of Science and Technology of China
University of Southern Maine
University of St Andrews
University of Suffolk
University of Szeged
University of Technology Sydney
University of Tehran
University of Texas at Austin
University of Texas at Dallas
University of Texas Rio Grande Valley
University of Warsaw
University of Washington
University of Waterloo
University of Wisconsin Madison
Univerzita Komenského v Bratislave
Vardhaman College of Engineering
Vardhman Mahaveer Open University
Vietnamese-German University
Vignana Jyothi Institute Of Management
Wageningen University
West Virginia University
Western University
Xavier University Bhubaneswar
Xi'an Jiaotong Liverpool University
Xiamen University
Yeshiva University
Yonsei University
Yunnan University
Zhejiang University

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

BibTeX entry for citing the book

@article{zhang2021dive,
    title={Dive into Deep Learning},
    author={Zhang, Aston and Lipton, Zachary C. and Li, Mu and Smola, Alexander J.},
    journal={arXiv preprint arXiv:2106.11342},
    year={2021}
}

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. Characteristics
  • 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
  • 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 Cues
  • 10.2. Attention Pooling: Nadaraya-Watson Kernel Regression
  • 10.3. Attention Scoring Functions
  • 10.4. Bahdanau Attention
  • 10.5. Multi-Head Attention
  • 10.6. Self-Attention and Positional Encoding
  • 10.7. 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
  • 13.8. Region-based CNNs (R-CNNs)
  • 13.9. Semantic Segmentation and the Dataset
  • 13.10. Transposed Convolution
  • 13.11. Fully Convolutional Networks
  • 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 Embeddings
  • 14.4. Pretraining word2vec
  • 14.5. Word Embedding with Global Vectors (GloVe)
  • 14.6. Subword Embedding
  • 14.7. Word Similarity and Analogy
  • 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