Titlebook: Deep Learning and Physics; Akinori Tanaka,Akio Tomiya,Koji Hashimoto Book 2021 The Editor(s) (if applicable) and The Author(s), under excl

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Forewords: Machine Learning and Physics,re 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 inform

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Basics of Neural Networkstput, and giving the network is equivalent to giving a function called an error function in the case of supervised learning. By considering the output as dynamical degrees of freedom and the input as an external field, various neural networks and their deepened versions are born from simple Hamilton

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Unsupervised Deep Learning answer” network given in Chap. 3, but rather the network itself giving the probability distribution of the input. Boltzmann machines have historically been the cornerstone of neural networks and are given by the Hamiltonian statistical mechanics of multi-particle spin systems. It is an important br

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Detection of Phase Transition by Machinesions be found by deep learning?”. Understanding phases is one of the most important subjects in physics. Can machine learning really discover the thermal phase transition in the basic physical system: Ising model?