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Practical MATLAB Deep Learning

A Project-Based Approach

Authors: Paluszek, Michael, Thomas, Stephanie

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  • Utilizes real world examples in MATLAB for major applications of deep learning and AI 
  • Comes with complete working MATLAB source code
  • Shows how to use MATLAB graphics and visualization tools for deep learning
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eBook $24.99
price for USA in USD
  • ISBN 978-1-4842-5124-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
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  • Immediate eBook download after purchase
Softcover $32.99
price for USA in USD
  • ISBN 978-1-4842-5123-2
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  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
About this book

Harness the power of MATLAB for deep-learning challenges. This book provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. You’ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. 
Along the way, you'll learn to model complex systems, including the stock market, natural language, and angles-only orbit determination. You’ll cover dynamics and control, and integrate deep-learning algorithms and approaches using MATLAB. You'll also apply deep learning to aircraft navigation using images.  
Finally, you'll carry out classification of ballet pirouettes using an inertial measurement unit to experiment with MATLAB's hardware capabilities.  

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
Who This Book Is For 
Engineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.

About the authors

Michael Paluszek is the co-author of MATLAB Recipes published by Apress. He is President of Princeton Satellite Systems, Inc. (PSS) in Plainsboro, New Jersey. Mr. Paluszek founded PSS in 1992 to provide aerospace consulting services. He used MATLAB to develop the control system and simulation for the Indostar-1 geosynschronous communications satellite, resulting in the launch of PSS' first commercial MATLAB toolbox, the Spacecraft Control Toolbox, in 1995. Since then he has developed toolboxes and software packages for aircraft, submarines, robotics, and fusion propulsion, resulting in PSS' current extensive product line. He is currently leading an Army research contract for precision attitude control of small satellites and working with the Princeton Plasma Physics Laboratory on a compact nuclear fusion reactor for energy generation and propulsion. Prior to founding PSS, Mr. Paluszek was an engineer at GE Astro Space in East Windsor, NJ. At GE he designed the Global Geospace Science Polar despun platform control system and led the design of the GPS IIR attitude control system, the Inmarsat-3 attitude control systems and the Mars Observer delta-V control system, leveraging MATLAB for control design. Mr. Paluszek also worked on the attitude determination system for the DMSP meteorological satellites. Mr. Paluszek flew communication satellites on over twelve satellite launches, including the GSTAR III recovery, the first transfer of a satellite to an operational orbit using electric thrusters. At Draper Laboratory Mr. Paluszek worked on the Space Shuttle, Space Station and submarine navigation. His Space Station work included designing of Control Moment Gyro based control systems for attitude control. Mr. Paluszek received his bachelors in Electrical Engineering, and master's and engineer’s degrees in Aeronautics and Astronautics from the Massachusetts Institute of Technology. He is author of numerous papers and has over a dozen U.S. Patents.

Stephanie Thomas is the co-author of MATLAB Recipes, published by Apress. She received her bachelor's and master's degrees in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 1999 and 2001. Ms. Thomas was introduced to PSS' Spacecraft Control Toolbox for MATLAB during a summer internship in 1996 and has been using MATLAB for aerospace analysis ever since. She built a simulation of a lunar transfer vehicle in C++, LunarPilot, during the same internship. In her nearly 20 years of MATLAB experience, she has developed many software tools including the Solar Sail Module for the Spacecraft Control Toolbox; a proximity satellite operations toolbox for the Air Force; collision monitoring Simulink blocks for the Prisma satellite mission; and launch vehicle analysis tools in MATLAB and Java, to name a few. She has developed novel methods for space situation assessment such as a numeric approach to assessing the general rendezvous problem between any two satellites implemented in both MATLAB and C++. Ms. Thomas has contributed to PSS' Attitude and Orbit Control textbook, featuring examples using the Spacecraft Control Toolbox, and written many software User's Guides. She has conducted SCT training for engineers from diverse locales such as Australia, Canada, Brazil, and Thailand and has performed MATLAB consulting for NASA, the Air Force, and the European Space Agency.

Table of contents (12 chapters)

Table of contents (12 chapters)
  • What Is Deep Learning?

    Pages 1-24

    Paluszek, Michael (et al.)

  • MATLAB Machine Learning Toolboxes

    Pages 25-41

    Paluszek, Michael (et al.)

  • Finding Circles with Deep Learning

    Pages 43-63

    Paluszek, Michael (et al.)

  • Classifying Movies

    Pages 65-76

    Paluszek, Michael (et al.)

  • Algorithmic Deep Learning

    Pages 77-90

    Paluszek, Michael (et al.)

Buy this book

eBook $24.99
price for USA in USD
  • ISBN 978-1-4842-5124-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $32.99
price for USA in USD
  • ISBN 978-1-4842-5123-2
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Practical MATLAB Deep Learning
Book Subtitle
A Project-Based Approach
Authors
Copyright
2020
Publisher
Apress
Copyright Holder
Michael Paluszek and Stephanie Thomas
eBook ISBN
978-1-4842-5124-9
DOI
10.1007/978-1-4842-5124-9
Softcover ISBN
978-1-4842-5123-2
Edition Number
1
Number of Pages
XV, 252
Number of Illustrations
11 b/w illustrations, 100 illustrations in colour
Topics

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