Max Planck Institute for Chemical Physics of Solids - Library Catalog

Machine learning with neural networks an introduction for scientists and engineers Bernhard Mehlig, University of Gothenburg, Sweden

By: Contributor(s): Material type: TextTextLanguage: English Publisher: Cambridge, United Kingdom New York, NY Port Melbourne, Australia New Delhi, India Singapore Cambridge University Press 2022Description: ix, 249 Seiten Illustrationen, DiagrammeContent type:
  • Text
Media type:
  • ohne Hilfsmittel zu benutzen
Carrier type:
  • Band
ISBN:
  • 9781108494939
Subject(s): Additional physical formats: No title; Erscheint auch als: Machine learning with neural networksDDC classification:
  • 006.3/2
LOC classification:
  • QA76.87
Other classification:
  • ST 300
  • ST 304
  • 54.72
Online resources: Summary: "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"--
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Bibliography: Seite 227-237

"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"--

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