Deep Learning and Physics [E-Book] / by Akinori Tanaka, Akio Tomiya, Koji Hashimoto.
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 knowin...
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Full text |
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Personal Name(s): | Tanaka, Akinori, author |
Hashimoto, Koji, author / Tomiya, Akio, author | |
Edition: |
1st edition 2021. |
Imprint: |
Singapore :
Springer,
2021
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Physical Description: |
XIII, 207 pages 46 illustrations, 29 illustrations in color (online resource) |
Note: |
englisch |
ISBN: |
9789813361089 |
DOI: |
10.1007/978-981-33-6108-9 |
Series Title: |
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Mathematical Physics Studies
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Classification: |
- Chapter 1: Forewords: Machine learning and physics
- Part I Physical view of deep learning
- Chapter 2: Introduction to machine learning
- Chapter 3: Basics of neural networks
- Chapter 4: Advanced neural networks
- Chapter 5: Sampling
- Chapter 6: Unsupervised deep learning
- Part II Applications to physics
- Chapter 7: Inverse problems in physics
- Chapter 8: Detection of phase transition by machines
- Chapter 9: Dynamical systems and neural networks
- Chapter 10: Spinglass and neural networks
- Chapter 11: Quantum manybody systems, tensor networks and neural networks
- Chapter 12: Application to superstring theory
- Chapter 13: Epilogue
- Bibliography
- Index.