Deep Learning in Science

Pierre Baldi

• Publisher: Cambridge University Press
• Year: 2021
• Print ISBN: 9781108845359
• eBook ISBN: 9781108960748
• Topic: Computer Science
• Subtopic: Computer Vision & Pattern Recognition
• Index: Computer Science

This is the first rigorous, self-contained treatment of the theory of deep learning. Starting with the foundations of the theory and building it up, this is essential reading for any scientists, instructors, and students interested in artificial intelligence and deep learning. It provides guidance on how to think about scientific questions, and leads readers through the history of the field and its fundamental connections to neuroscience. The author discusses many applications to beautiful problems in the natural sciences, in physics, chemistry, and biomedicine. Examples include the search for exotic particles and dark matter in experimental physics, the prediction of molecular properties and reaction outcomes in chemistry, and the prediction of protein structures and the diagnostic analysis of biomedical images in the natural sciences. The text is accompanied by a full set of exercises at different difficulty levels and encourages out-of-the-box thinking.

Table of contents

• Cover
• Half-title
• Title page
• Copyright information
• Dedication
• Contents
• Preface
• 1 Introduction
• 2 Basic Concepts
• 3 Shallow Networks and Shallow Learning
• 4 Two-Layer Networks and Universal Approximation
• 5 Autoencoders
• 6 Deep Networks and Backpropagation
• 7 The Local Learning Principle
• 8 The Deep Learning Channel
• 9 Recurrent Networks
• 10 Recursive Networks
• 11 Applications in Physics
• 12 Applications in Chemistry
• 13 Applications in Biology and Medicine
• 14 Conclusion
• Appendix A einforcement Learning and Deep Reinforcement Learning
• Appendix B Hints and Remarks for Selected Exercises
• References
• Index