Chapter 1
Forewords: Machine Learning
and Physics
Abstract What is the relationship between machine learning and physics? First
let us start by experiencing why machine learning and physics can be related.
There is a concept that bridges between physics and machine learning: that is
information. Physics and information theory have been mutually involved for a
long time. Also, machine learning is based on information theory. Learning is about
passing information and recreating relationships between information, and finding
information spontaneously. Therefore, in machine learning, it is necessary to use
information theory that flexibly deal with the amount of information, and as a
result, machine learning is closely related to the system of information theory. This
chapter explores the relationship between physics, information theory, and machine
learning, the core concepts in this book.
What is the relationship between machine learning and physics? We’ll take a closer
look at that in this book, but first let us start by experiencing why machine learning
and physics can be related. There is a concept that bridges between physics and
machine learning: that is information.
Physics and information theory have been mutually involved for a long time,
and the relationship is still widely and deeply developed. Also, machine learning is
based on information theory. Learning is about passing information and recreating
relationships between information, and finding information spontaneously. Therefore, in machine learning and deep learning, it is necessary to use information theory
that flexibly deals with the amount of information, and as a result, machine learning
is closely related to the system of information theory.
As the reader can imagine from these things, machine learning and physics
should have some big relationship with “information” as an intermediate medium.
One of the goals of this book is to clarify this firm bridge. Figure 1.1 shows a
conceptual diagram.
This chapter explores the relationship between physics, information theory, and
machine learning, the core concepts in this book. Let us explain how the titles of
this book, “Deep Learning” and “Physics” are related.
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
A. Tanaka et al., Deep Learning and Physics, Mathematical Physics Studies,
https://doi.org/10.1007/978-981-33-6108-9_1
1
Forewords: Machine Learning
and Physics
Abstract What is the relationship between machine learning and physics? First
let us start by experiencing why machine learning and physics can be related.
There is a concept that bridges between physics and machine learning: that is
information. Physics and information theory have been mutually involved for a
long time. Also, machine learning is based on information theory. Learning is about
passing information and recreating relationships between information, and finding
information spontaneously. Therefore, in machine learning, it is necessary to use
information theory that flexibly deal with the amount of information, and as a
result, machine learning is closely related to the system of information theory. This
chapter explores the relationship between physics, information theory, and machine
learning, the core concepts in this book.
What is the relationship between machine learning and physics? We’ll take a closer
look at that in this book, but first let us start by experiencing why machine learning
and physics can be related. There is a concept that bridges between physics and
machine learning: that is information.
Physics and information theory have been mutually involved for a long time,
and the relationship is still widely and deeply developed. Also, machine learning is
based on information theory. Learning is about passing information and recreating
relationships between information, and finding information spontaneously. Therefore, in machine learning and deep learning, it is necessary to use information theory
that flexibly deals with the amount of information, and as a result, machine learning
is closely related to the system of information theory.
As the reader can imagine from these things, machine learning and physics
should have some big relationship with “information” as an intermediate medium.
One of the goals of this book is to clarify this firm bridge. Figure 1.1 shows a
conceptual diagram.
This chapter explores the relationship between physics, information theory, and
machine learning, the core concepts in this book. Let us explain how the titles of
this book, “Deep Learning” and “Physics” are related.
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
A. Tanaka et al., Deep Learning and Physics, Mathematical Physics Studies,
https://doi.org/10.1007/978-981-33-6108-9_1
1