What is deep learning for those who study physics? Is it completely
different from physics? Or is it similar?
In recent years, machine learning, including deep learning, has begun to
be used in various physics studies. Why is that? Is knowing physics
useful in machine learning? Conversely, is knowing machine learning
useful in physics?
This book is devoted to answers of these questions. Starting with basic
ideas of physics, neural networks are derived naturally. And you can
learn the concepts of deep learning through the words of physics.
In fact, the foundation of machine learning can be attributed to
physical concepts. Hamiltonians that determine physical systems
characterize various machine learning structures. Statistical physics
given by Hamiltonians defines machine learning by neural networks.
Furthermore, solving inverse problems in physics through machine
learning and generalization essentially provides progress and even
revolutions in physics. For these reasons, in recent years
interdisciplinary research in machine learning and physics has been
expanding dramatically.
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. The concepts of statistical mechanics
and the bracket notation of quantum mechanics, which are explained in
columns, are used to explain deep learning frameworks.
We encourage you to explore this new active field of machine learning
and physics, with this book as a map of the continent to be explored.
