Understanding Deep Learning by Simon J.D. Prince, ISBN-13: 978-0262048644



Understanding Deep Learning by Simon J.D. Prince, ISBN-13: 978-0262048644

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  • Publisher: ‎ The MIT Press (December 5, 2023)
  • Language: ‎ English
  • 544 pages
  • ISBN-10: ‎ 0262048647
  • ISBN-13: ‎ 978-0262048644

An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.

Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.

– Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models
– Short, focused chapters progress in complexity, easing students into difficult concepts
– Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models
– Streamlined presentation separates critical ideas from background context and extraneous detail
– Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible
– Programming exercises offered in accompanying Python Notebooks

Table of Contents:

Chapter 1: Introduction
Chapter 2: Supervised learning
Chapter 3: Shallow neural networks
Chapter 4: Deep neural networks
Chapter 5: Loss functions
Chapter 6: Fitting models
Chapter 7: Gradients and initialization
Chapter 8: Measuring performance
Chapter 9: Regularization
Chapter 10: Convolutional networks
Chapter 11: Residual networks
Chapter 12: Transformers
Chapter 13: Graph neural networks
Chapter 14: Unsupervised learning
Chapter 15: Generative Adversarial Networks
Chapter 16: Normalizing flows
Chapter 17: Variational autoencoders
Chapter 18: Diffusion models
Chapter 19: Reinforcement learning
Chapter 20: Why does deep learning work?
Chapter 21: Deep learning and ethics
Appendix A: Notation
Appendix B: Mathematics
Appendix C: Probability

Simon J. D. Prince is Honorary Professor of Computer Science at the University of Bath and author of Computer Vision: Models, Learning and Inference. A research scientist specializing in artificial intelligence and deep learning, he has led teams of research scientists in academia and industry at Anthropics Technologies Ltd, Borealis AI, and elsewhere.

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