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Deep Learning for Physics Research

by Erdmann Martin

Deep learning methods for physics research

Regular price $105.79 USD
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Hardcover New English

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AlbakiReads Editorial

Deep learning methods for physics research

Martin’s textbook connects deep learning with a central feature of physics research: drawing knowledge from data. It introduces what deep learning involves and considers its potential in both basic and applied physics projects. The coverage combines foundational material with advanced applications, while hands-on exercises let readers implement deep networks; the description notes that Python code and training data are available for download. Linear algebra and parameter optimization are presented as sufficient starting knowledge, giving the book a defined technical entry point without requiring prior deep-learning expertise.

Good fit for readers who enjoy

  • Physics students seeking an introduction to deep learning in research contexts
  • Physicists considering deep-learning methods for scientific projects
  • Readers with familiarity in linear algebra and parameter optimization who want practical exercises

Themes

Deep learning in physicsData-driven researchBasic and applied applicationsPractical implementation

Based on publisher information and book metadata.

Book overview

A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research.

 

This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning.

 

Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded.

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