Introduction to Machine Learning Physics [単行本]

Introduction to Machine Learning Physics の 商品概要

• 目次

  A Machine Learning and Physics
  
  A1. Linear Models
  A2. Neural Networks (NN)
  A3. Symmetry and Machine Learning: Convolution and Equivariant NN
  A4. Classical Mechanics and Machine Learning: Neural Networks and Differential Equations
  A5. Quantum Mechanics and Machine Learning
  
  B Machine Learning Models and Physics
  
  B1. Transformer
  B2. Diffusion Models and Path Integrals
  B3. Mechanism Behind Machine Learning
  B4. Large Language Models and Science

• 出版社からのコメント

  物理学と人工知能・機械学習のコラボレーションを学ぶ入門テキスト。全文英語。

• 内容紹介

  An introductory textbook that examines the interplay between physics and AI/machine learning. Aimed at physics students, it provides a smooth entry into machine learning and explores the collaborative relationship between the two fields. [language: English]
  
  【主な目次】
  A Machine Learning and Physics
  
  A1. Linear Models
  A2. Neural Networks (NN)
  A3. Symmetry and Machine Learning: Convolution and Equivariant NN
  A4. Classical Mechanics and Machine Learning: Neural Networks and Differential Equations
  A5. Quantum Mechanics and Machine Learning
  
  B Machine Learning Models and Physics
  
  B1. Transformer
  B2. Diffusion Models and Path Integrals
  B3. Mechanism Behind Machine Learning
  B4. Large Language Models and Science

• 著者について

  橋本　幸士 (ハシモト　コウジ)
  橋本　幸士： 京都大学大学院理学研究科教授、学術変革領域研究A「『学習物理学』の創成」領域代表

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