Practical MATLAB Deep Learning: A Projects-Based Approach by Michael Paluszek (E

Description: Practical MATLAB Deep Learning by Michael Paluszek, Stephanie Thomas, Eric Ham 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 MATLABs deep-learning toolboxes. In this book, youll 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, youll learn to model complex systems and apply deep learning to problems in those areas. Applications include:Aircraft navigationAn aircraft that lands on Titan, the moon of Saturn, using reinforcement learningStock market predictionNatural language processingMusic creation usng generative deep learningPlasma controlEarth sensor processing for spacecraftMATLAB Bluetooth data acquisition applied to dance physics What You Will LearnExplore deep learning using MATLAB and compare it to algorithmsWrite a deep learning function in MATLAB and train it with examplesUse MATLAB toolboxes related to deep learningImplement tokamak disruption predictionNow includes reinforcement learningWho This Book Is For Engineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB. FORMAT Paperback LANGUAGE English CONDITION Brand New Back Cover 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 MATLABs deep-learning toolboxes. In this book, youll 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, youll 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 You will: 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 Author Biography 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 masters and engineers 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 bachelors and masters 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 Users 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.Eric Ham is a a Technical Specialist, Princeton Satellite Systems. His expertise lies with deep learning, programming using MATLAB, C++ and related. 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. Feature Utilizes real world examples in MATLAB for major applications of deep learning and AI Adds 3 new chapters and comes with complete working MATLAB source code Shows how to use MATLAB graphics and visualization tools for deep learning Details ISBN1484279115 Author Eric Ham Short Title Practical MATLAB Deep Learning Language English Year 2022 Edition 2nd ISBN-10 1484279115 ISBN-13 9781484279113 Format Paperback Subtitle A Projects-Based Approach Publisher APress Imprint APress Place of Publication Berkley Country of Publication United States Pages 329 Publication Date 2022-09-11 AU Release Date 2022-09-11 NZ Release Date 2022-09-11 US Release Date 2022-09-11 UK Release Date 2022-09-11 Edited by Jessica Schwarz Birth 1974 Affiliation European University Viadrina, Germany Position journalist Qualifications Ph.D. Illustrations 129 Illustrations, color; 12 Illustrations, black and white; XIX, 329 p. 141 illus., 129 illus. in color. Edition Description 2nd ed. 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