Introduction to Machine Learning Physics

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 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

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  Dブレーン　増補新装版 : 超弦理論の高次元物体が描く世界像

  橋本　幸士

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  学習物理学入門

  橋本　幸士