Dive into Deep Learning¶
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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.
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.
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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.
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Leading Unviersity
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Make School
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Sungkyunkwan University
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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
- 1. Introduction
- 2. Preliminaries
- 3. Linear Neural Networks
- 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
- 6. Convolutional Neural Networks
- 7. Modern Convolutional Neural Networks
- 8. Recurrent Neural Networks
- 9. Modern Recurrent Neural Networks
- 10. Attention Mechanisms
- 11. Optimization Algorithms
- 12. Computational Performance
- 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
- 18. Appendix: Mathematics for Deep Learning
- 19. Appendix: Tools for Deep Learning