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  3. Machine Learning with Neural Networks
  • Cited by 61
Publisher:
Cambridge University Press
Online publication date:
October 2021
Print publication year:
2021
Online ISBN:
9781108860604
DOI:
https://doi.org/10.1017/9781108860604
Subjects:
Physics and Astronomy, Computer Science, Pattern Recognition and Machine Learning, Statistical Physics
Information

Book description

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.

Reviews

'… for someone who wants to understand neural networks at a fundamental level, or to code something from scratch, or to make some advances in the core ideas and develop the field as a result, then this book will give you the theoretical framework for doing just that.'

Matt Probert Source: Contemporary Physics

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