Machine Learning Refined

Foundations, Algorithms, and Applications

Nonfiction, Science & Nature, Technology, Engineering, Mathematics, Computers, General Computing
Cover of the book Machine Learning Refined by Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos, Cambridge University Press
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Author: Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos ISBN: 9781316557631
Publisher: Cambridge University Press Publication: September 8, 2016
Imprint: Cambridge University Press Language: English
Author: Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos
ISBN: 9781316557631
Publisher: Cambridge University Press
Publication: September 8, 2016
Imprint: Cambridge University Press
Language: English

Providing a unique approach to machine learning, this text contains fresh and intuitive, yet rigorous, descriptions of all fundamental concepts necessary to conduct research, build products, tinker, and play. By prioritizing geometric intuition, algorithmic thinking, and practical real world applications in disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology, this text provides readers with both a lucid understanding of foundational material as well as the practical tools needed to solve real-world problems. With in-depth Python and MATLAB/OCTAVE-based computational exercises and a complete treatment of cutting edge numerical optimization techniques, this is an essential resource for students and an ideal reference for researchers and practitioners working in machine learning, computer science, electrical engineering, signal processing, and numerical optimization.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Providing a unique approach to machine learning, this text contains fresh and intuitive, yet rigorous, descriptions of all fundamental concepts necessary to conduct research, build products, tinker, and play. By prioritizing geometric intuition, algorithmic thinking, and practical real world applications in disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology, this text provides readers with both a lucid understanding of foundational material as well as the practical tools needed to solve real-world problems. With in-depth Python and MATLAB/OCTAVE-based computational exercises and a complete treatment of cutting edge numerical optimization techniques, this is an essential resource for students and an ideal reference for researchers and practitioners working in machine learning, computer science, electrical engineering, signal processing, and numerical optimization.

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