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