Machine Learning with Neural Networks
Machine Learning with Neural Networks
£42.00
GBPThis 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.
- Accessible and self-contained approach to the theoretical foundations of machine learning, and its modern applications in science and engineering
- Suitable for teaching advanced undergraduate or graduate courses on this topic for science, mathematics and engineering students
- Numerous end of chapter exercises expand and reinforce key concepts
Reviews & endorsements
'… for someone who wants to understand neural networks at a fundamental level, or to code something from scratch, or to make some advances in the core ideas and develop the field as a result, then this book will give you the theoretical framework for doing just that.' Matt Probert, Contemporary Physics
Product details
October 2021Hardback
9781108494939
260 pages
250 × 174 × 16 mm
0.65kg
Available
Table of Contents
- 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.
