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Image of - Machine Learning For The Physical Sciences Fundamentals And Prototyping With Julia | Paperback
Machine Learning For The Physical Sciences Fundamentals And Prototyping With Julia | Paperback

Machine Learning For The Physical Sciences Fundamentals And Prototyping With Julia | Paperback

by Cunha C.R.D.

Machine learning is an exciting topic with a myriad of applications. However, most textbooks are targeted towards computer science students. This, however, creates a complication for scientists across the physical sciences that also want to understand the main concepts of machine learning and look ahead to applica- tions and advancements in their fields.This textbook bridges this gap, providing an introduction to the mathematical foundations for the main algorithms used in machine learning for those from the physical sciences, without a formal background in computer science. It demon- strates how machine learning can be used to solve problems in physics and engineering, targeting senior undergraduate and graduate students in physics and electrical engineering, alongside advanced researchers.All codes are available on the author's website: C¢Lab (nau.edu)They are also available on GitHub: https://github.com/StxGuy/MachineLearningKey Features:Includes detailed algorithms.Supplemented by codes in Julia: a high-performing language and one that is easy to read for those in the natural sciences.All algorithms are presented with a good mathematical background.

Highlights

  • binding-icon

    9781032395234

    ISBN:

  • binding-icon

    Cunha C.R.D.

    Author:

  • binding-icon

    266

    Pages:

  • binding-icon

    3500 gm

    Weight:

  • langauage-icon

    English

    Language:

  • date-icon

    2024

    Year:

  • edition-icon

    1st Edition

    Edition:

  • binding-icon

    Paperback

    Binding:

₹6586

₹8232

Machine learning is an exciting topic with a myriad of applications. However, most textbooks are targeted towards computer science students. This, however, creates a complication for scientists across the physical sciences that also want to understand the main concepts of machine learning and look ahead to applica- tions and advancements in their fields.This textbook bridges this gap, providing an introduction to the mathematical foundations for the main algorithms used in machine learning for those from the physical sciences, without a formal background in computer science. It demon- strates how machine learning can be used to solve problems in physics and engineering, targeting senior undergraduate and graduate students in physics and electrical engineering, alongside advanced researchers.All codes are available on the author's website: C¢Lab (nau.edu)They are also available on GitHub: https://github.com/StxGuy/MachineLearningKey Features:Includes detailed algorithms.Supplemented by codes in Julia: a high-performing language and one that is easy to read for those in the natural sciences.All algorithms are presented with a good mathematical background.

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