This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. Fundamental physical and mathematical principles of the topic are described alongside current applications in science and engineering. Numerous exercises expand and reinforce key concepts within the book.
This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. Fundamental physical and mathematical principles of the topic are described alongside current applications in science and engineering. Numerous exercises expand and reinforce key concepts within the book.
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.
Inhaltsangabe
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.
