Our latest textbook on MATLAB programming is now available from Apress. Practical MATLAB Deep Learning, A Projects-Based Approach, is in its second edition. It is available in both electronic and hard copy from SpringerLink.
New coauthor Eric Ham, a deep learning research specialist, joins Michael Paluszek and Stephanie Thomas. Mr. Ham led the development of a new chapter on generative modeling of music.
The software is available from GitHub:
About the Book
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:
- NEW: An aircraft that lands on Titan, the moon of Saturn, using reinforcement learning
- NEW: Music creation using generative deep learning
- NEW: Earth sensor processing for spacecraft
- Aircraft navigation
- MATLAB Bluetooth data acquisition applied to dance physics
- Stock market prediction
- Natural language processing
- Plasma control
- 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
The book primarily features the Deep Learning Toolbox and the Reinforcement Learning toolboxes. Some examples in the book feature other MathWorks toolboxes, include the Instrument Control toolbox, Optimization toolbox, Statistics and Machine Learning, and Image Processing toolbox.
Our other books
This new second edition joins our other books available from Apress: