Practical MATLAB Deep Learning: A Projects-Based Approach 2nd Edition

Practical MATLAB Deep Learning: A Projects-Based Approach 2nd Edition – Ebook Instant Download/Delivery ISBN(s): 9781484279120,9781484279113,1484279115,1484279123

Product details:

• ISBN-10: 1484279123
• ISBN-13: 9781484279113
• Author: 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 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 contents:

1. What Is Deep Learning?
2. MATLAB Machine Learning Toolboxes
3. Finding Circles with Deep Learning
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

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