Dive into Deep Learning (with PyTorch)
by Aston Zhang, Zachary Lipton, Mu Li, Alexander Smola
Combines rigorous mathematical foundations with hands-on, executable code examples in a free, interactive format that takes you from deep learning fundamentals to state-of-the-art techniques, making it ideal whether you're a student, researcher, or practitioner looking to truly understand and implement neural networks.
- Introduction
- Preliminaries
- Linear Neural Networks for Regression
- Linear Neural Networks for Classification
- Multilayer Perceptrons
- Builders' Guide
- Convolutional Neural Networks
- Modern Convolutional Neural Networks
- Recurrent Neural Networks
- Modern Recurrent Neural Networks
- Attention Mechanisms and Transformers
- Optimization Algorithms
- Computational Performance
- Computer Vision
- Natural Language Processing: Pretraining
- Natural Language Processing: Applications
- Reinforcement Learning
- Gaussian Processes
- Hyperparameter Optimization
- Generative Adversarial Networks
- Recommender Systems
- Appendix: Mathematics for Deep Learning
- Appendix: Tools for Deep Learning