Overview
- 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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About this book
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.
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Keywords
Table of contents (12 chapters)
Authors and Affiliations
About the authors
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.
Bibliographic Information
Book Title: Practical MATLAB Deep Learning
Book Subtitle: A Project-Based Approach
Authors: Michael Paluszek, Stephanie Thomas
DOI: https://doi.org/10.1007/978-1-4842-5124-9
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Michael Paluszek and Stephanie Thomas 2020
eBook ISBN: 978-1-4842-5124-9Published: 07 February 2020
Edition Number: 1
Number of Pages: XV, 252
Number of Illustrations: 11 b/w illustrations, 100 illustrations in colour
Topics: Programming Languages, Compilers, Interpreters, Artificial Intelligence, Hardware and Maker, Mathematics of Computing, Programming Techniques