Bokus
Bernhard Mehlig - Machine Learning with Neural Networks, Inbunden

Inbunden, Engelska, 2021

Machine Learning with Neural Networks

An Introduction for Scientists and Engineers

Av Bernhard Mehlig

692 kr

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Beskrivning
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.
Produktinformation
  • Utgivningsdatum: 2021-10-28
  • Mått: 174 x 250 x 16 mm
  • Vikt: 650 g
  • Format: Inbunden
  • Språk: Engelska
  • Antal sidor: 260
  • Förlag: Cambridge University Press
  • ISBN: 9781108494939
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Mer om författaren

Bernhard Mehlig is Professor in Physics at the University of Gothenburg, Sweden. His research is focused on statistical physics of complex systems, and he has published extensively in this area. In 2010, he was awarded the prestigious Göran Gustafsson prize in physics for his outstanding research in statistical physics. He has taught a course on machine learning for more than 15 years at the University of Gothenburg.

Recensioner i media

'… 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, Contemporary Physics

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