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Machine Learning For Physicists
Machine Learning For Physicists
Knygos.lt klubas Knygos.lt nariams
289,58 €
-30%
Įprastai
413,69 €
  • Išsiųsime per 12–18 d.d.
This book presents ML concepts with a hands-on approach for physicists. The goal is to both educate and enable a larger part of the community with these skills. This will lead to wider applications of modern ML techniques in physics. Accessible to physical science students, the book assumes a familiarity with statistical physics but little in the way of specialised computer science background. All chapters start with a simple introduction to the basics and the foundations, followed by some exam…

Machine Learning For Physicists (el. knyga) (skaityta knyga) | knygos.lt

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Aprašymas

This book presents ML concepts with a hands-on approach for physicists. The goal is to both educate and enable a larger part of the community with these skills. This will lead to wider applications of modern ML techniques in physics. Accessible to physical science students, the book assumes a familiarity with statistical physics but little in the way of specialised computer science background. All chapters start with a simple introduction to the basics and the foundations, followed by some examples and then proceeds to provide concrete examples with associated codes from a GitHub repository. Many of the code examples provided can be used as is or with suitable modification by the students for their own applications.


Key Features:


  • Practical Hands-on approach: enables the reader to use machine learning


  • Includes code and accompanying online resources


  • Practical examples for modern research and uses case studies


  • Written in a language accessible by physics students


  • Complete one-semester course


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Daugiau

This book presents ML concepts with a hands-on approach for physicists. The goal is to both educate and enable a larger part of the community with these skills. This will lead to wider applications of modern ML techniques in physics. Accessible to physical science students, the book assumes a familiarity with statistical physics but little in the way of specialised computer science background. All chapters start with a simple introduction to the basics and the foundations, followed by some examples and then proceeds to provide concrete examples with associated codes from a GitHub repository. Many of the code examples provided can be used as is or with suitable modification by the students for their own applications.


Key Features:


  • Practical Hands-on approach: enables the reader to use machine learning


  • Includes code and accompanying online resources


  • Practical examples for modern research and uses case studies


  • Written in a language accessible by physics students


  • Complete one-semester course


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