Machine Learning with Neural Networks: An Introduction for Scientists and Engineers
Cambridge University Press
Edition: New, 10/28/2021
EAN 9781108494939, ISBN10: 1108494935
Hardcover, 260 pages, 24.9 x 21.1 x 1.5 cm
Language: English
Originally published in English
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.
Acknowledgements. 1. Introduction. Part I. Hopfield Networks
2. Deterministic Hopfield networks
3. Stochastic Hopfield networks
4. The Boltzmann distribution. Part II. Supervised Learning
5. Perceptrons
6. Stochastic gradient descent
7. Deep learning
8. Convolutional networks
9. Supervised recurrent networks. Part III. Learning Without Labels
10. Unsupervised learning
11. Reinforcement learning. Bibliography. Author Index. Index.