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Practical Matlab Deep Learning eBook

A Projects-Based Approach

by Stephanie Thomas, Eric Ham e Michael Paluszek
Book eBook
language: english
Publisher: Apress, September of 2022 ‧
72,86€
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Ebook for ADE

Harness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that provides an introduction to deep learning and using MATLAB''s deep-learning toolboxes. In this book, you’ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. This edition includes new and expanded projects, and covers generative deep learning and reinforcement learning.

Over the course of the book, you''ll learn to model complex systems and apply deep learning to problems in those areas. Applications include:

  • Aircraft navigation
  • An aircraft that lands on Titan, the moon of Saturn, using reinforcement learning
  • Stock market prediction
  • Natural language processing
  • Music creation usng generative deep learning
  • Plasma control
  • Earth sensor processing for spacecraft
  • MATLAB Bluetooth data acquisition applied to dance physics  


What You Will Learn
  • Explore deep learning using MATLAB and compare it to algorithms
  • Write a deep learning function in MATLAB and train it with examples
  • Use MATLAB toolboxes related to deep learning
  • Implement tokamak disruption prediction
  • Now includes reinforcement learning
Who This Book Is For 

Engineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.

Practical Matlab Deep Learning

A Projects-Based Approach

by Stephanie Thomas, Eric Ham e Michael Paluszek

Property Description
ISBN: 9781484279120
Publisher: Apress
Release Date: September of 2022
Language: English
Format: eBook
File Format and Compatibility:
Collection: Professional And Applied Computing
Categories: eBooks in English > Science > Mathematics
eBooks in English > Computing > Other Applications
EAN: 9781484279120
Acessibilidade: Ver características de acessibilidade indicadas pelo editor