Deep learning for physics research reviews the research and applications of deep learning methods for physics students and physicists. It specifically looks at how deep learning and neural network applications can be used in their work.
The book presents up-to-date information in four sections. It also has a well-built website with example code snippets. The first section’s three chapters introduce deep learning concepts. The second section’s five chapters present information about applying deep learning to physics research. The third section is about the visualization of weights and activation functions, and the last section gives the reader an idea of some advanced concepts.
This book can be a good resource for scientists, researchers, engineers, and graduate students who have the required background in computer programming. They can use the book to expand their knowledge on the use of deep learning for physics and to get acquainted with the current literature in the field. Graduate classes can use it as a textbook, as there are exercises and further reading sections at the end of every chapter that will be useful to instructors. The book can also help readers better understand the physics discipline.
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