Machine Learning with Neural Networks: An Introduction for Scientists and Engineers

✍ Scribed by Bernhard Mehlig

Publisher: Cambridge University Press
Year: 2021
Tongue: English
Leaves: 261
Edition: New
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.

✦ Table of Contents

Cover
Frontmatter
Contents
Acknowledgements
1 Introduction
Part I Hopfield Networks
2 Deterministic Hopfield Networks
3 Stochastic Hopfield Networks
4 The Boltzmann Distribution
Part II Supervised Learning
5 Perceptrons
6 Stochastic Gradient Descent
7 Deep Learning
8 Convolutional Networks
9 Supervised Recurrent Networks
Part III Learning without Labels
10 Unsupervised Learning
11 Reinforcement Learning
Bibliography
Author Index
Subject Index
Series page

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