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The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

This book is written for anyone who wants to learn, understand, and apply the relationship between deep learning/machine learning and physics. All that is needed to read this book are the basic concepts in physics: energy and Hamiltonians.

This beginning graduate textbook teaches data science and machine learning methods for modeling, prediction, and control of complex systems.

This book provides a first course on deep learning in computational mechanics. The book starts with a short introduction to machine learning’s fundamental concepts before neural networks are explained thoroughly.

At its heart, this book is focused on providing an end-to-end framework—from design to application—of a more tractable model-based reinforcement learning technique.

... Systems with Applications”, HKGRF-14300319 with the project title “Shape-constrained Inference: Testing for Monotonicity”, and Direct Grant for Research 2014/15 ... Machine learning and control theory Chapter | 16 557 References.

In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject. This book describes how neural networks operate from the mathematical point of view.

This book takes you systematically through the core mathematical concepts you’ll need as a working data scientist: vector calculus, linear algebra, and Bayesian inference, all from a deep learning perspective.

This is relevant for many applications, such as model predictive control, that requires predicting the values for the whole receding horizon.