(Ebook PDF) Understanding Deep Learning 1st edition by Simon Prince 0262048647 9780262048644 Full chapter
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ISBN 10: 0262048647
ISBN 13: 978-0262048644
Author: Simon J. D. Prince
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:
1 Introduction
2 Supervised learning
3 Shallow neural networks
4 Deep neural networks
5 Loss functions
6 Fitting models
7 Gradients and initialization
8 Measuring performance
9 Regularization
10 Convolutional networks
11 Residual networks
12 Transformers
13 Graph neural networks
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