Mehlig, Bernhard

Machine learning with neural networks : an introduction for scientists and engineers - Cambridge : Cambridge University Press, 2022 - ix, 249 p. ; ill., 26 cm

Includes bibliographical references and index.

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.

9781108494939


Machine learning
Neural networks
Computer science
Activation Function
Backpropagation
Central-limit theorem
Cross-talk term
Decision boundary
Energy function
Feed-forward network
Hidden neuron
Hopfield network
Image Net
Markov chain
Mean-field theory
Noise level
Output neuron
Principle component
Q-table
Residwal network
Stochastic gradient descent
Vanishing gradient
Winning neoron
XOR problem
Boltzmann Machines

006.32 / MEH

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