Practical MATLAB Deep Learning〈2nd ed.〉 : A Projects-Based Approach(2)

個数:1
紙書籍版価格
¥11,751
  • 電子書籍
  • ポイントキャンペーン

Practical MATLAB Deep Learning〈2nd ed.〉 : A Projects-Based Approach(2)

  • 著者名:Paluszek, Michael/Thomas, Stephanie/Ham, Eric
  • 価格 ¥11,915 (本体¥10,832)
  • Apress(2022/09/10発売)
  • 春分の日の三連休!Kinoppy 電子書籍・電子洋書 全点ポイント30倍キャンペーン(~3/22)
  • ポイント 3,240pt (実際に付与されるポイントはご注文内容確認画面でご確認下さい)
  • 言語:ENG
  • ISBN:9781484279113
  • eISBN:9781484279120

ファイル: /

Description

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.

Table of Contents

1. What is deep learning?.- 2. MATLAB Toolboxes.- 3. Finding Circles.- 4. Classifying Movies.- 5. Algorithmic Deep Learning.- 6. Tokamak Disruption Detection.- 7. Classifying a Pirouette.- 8. Completing Sentences.- 9. Terrain Based Navigation.- 10. Stock Prediction.- 11. Image Classification.- 12. Orbit Determination.- 13. Earth Sensors.- 14. Generative Modeling of Music.- 15. Reinforcement Learning.- Bibliography.

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