{"product_id":"practical-matlab-deep-learning-a-projects-based-approach-9781484279113","title":"Practical MATLAB Deep Learning: A Projects-Based Approach","description":"\u003cp\u003eHarness 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.\u003c\/p\u003e \u003cp\u003eOver the course of the book, you'll learn to model complex systems and apply deep learning to problems in those areas. Applications include: \u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eAircraft navigation\u003c\/li\u003e\n\u003cli\u003eAn aircraft that lands on Titan, the moon of Saturn, using reinforcement learning\u003c\/li\u003e\n\u003cli\u003eStock market prediction\u003c\/li\u003e\n\u003cli\u003eNatural language processing\u003c\/li\u003e\n\u003cli\u003eMusic creation usng generative deep learning\u003c\/li\u003e\n\u003cli\u003ePlasma control\u003c\/li\u003e\n\u003cli\u003eEarth sensor processing for spacecraft\u003c\/li\u003e\n\u003cli\u003eMATLAB Bluetooth data acquisition applied to dance physics \u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cb\u003eWhat You Will Learn\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eExplore deep learning using MATLAB and compare it to algorithms\u003c\/li\u003e\n\u003cli\u003eWrite a deep learning function in MATLAB and train it with examples\u003c\/li\u003e\n\u003cli\u003eUse MATLAB toolboxes related to deep learning\u003c\/li\u003e\n\u003cli\u003eImplement tokamak disruption prediction\u003c\/li\u003e\n\u003cli\u003eNow includes reinforcement learning\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eWho This Book Is For \u003c\/b\u003e\u003cbr\u003eEngineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eMichael Paluszek\u003c\/b\u003e 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.\u003cbr\u003e\u003cb\u003eStephanie Thomas\u003c\/b\u003e 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.\u003cbr\u003e\u003cb\u003eEric Ham\u003c\/b\u003e is a a Technical Specialist, Princeton Satellite Systems. His expertise lies with deep learning, programming using MATLAB, C++ and related.\u003cbr\u003e","brand":"Apress","offers":[{"title":"Default Title","offer_id":50486634873106,"sku":"9781484279113","price":43.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_888e75b0-ea4a-4860-9a0d-add8ee8deef5.jpg?v=1730432064","url":"https:\/\/surprise-castle.myshopify.com\/products\/practical-matlab-deep-learning-a-projects-based-approach-9781484279113","provider":"Surprise Castle","version":"1.0","type":"link"}