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

[PDF eBook eTextbook] – **Available Instantly**

- 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:
**

Cover

Contents

Preface

Acknowledgments

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

Bibliography

Index

* 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.

**What makes us different?**

• Instant Download

• Always Competitive Pricing

• 100% Privacy

• FREE Sample Available

• 24-7 LIVE Customer Support

## Reviews

There are no reviews yet.