The Science of Deep Learning

Up-to-date guide to deep learning with unique content, rigorous math, unified notation, comprehensive algorithms, and high-quality figures.

Iddo Drori (Author)

9781108835084, Cambridge University Press

Hardback, published 18 August 2022

360 pages
25 x 17.5 x 2 cm, 0.82 kg

'Drori's textbook makes the learning curve for deep learning a whole lot easier to climb. It follows a rigid scientific narrative, accompanied by a trove of code examples and visualizations. These enable a truly multi-modal approach to learning that will allow many students to understand the material better and sets them on a path of exploration.' Joaquin Vanschoren, Assistant Professor of Machine Learning, Eindhoven University of Technology

The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.

Preface
Notation
Part I. Foundations: 1. Introduction
2. Forward and backpropagation
3. Optimization
4. Regularization
Part II. Architectures: 5. Convolutional neural networks
6. Sequence models
7. Graph neural networks
8. Transformers
Part III. Generative Models: 9. Generative adversarial networks
10. Variational autoencoders
Part IV. Reinforcement Learning: 11. Reinforcement learning
12. Deep reinforcement learning
Part V. Applications: 13. Applications
Appendices
References
Index.

Subject Areas: Artificial intelligence [UYQ], Optimization [PBU], Information theory [GPF]