Practical MATLAB Deep Learning
A Project-Based Approach
| Price | $54.96
|
| Rating |   |
| Authors | Michael Paluszek, Stephanie Thomas |
| Publisher | Apress |
| Published | 2020 |
| Pages | 252 |
| Language | English |
| Format | Paper book / ebook (PDF) |
| ISBN-10 | 1484251237 |
| ISBN-13 | 9781484251232 |
Harness the power of MATLAB for deep-learning challenges. This book provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. You'll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning.
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.
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.
- Michael Paluszek (5 books)
- Stephanie Thomas (5 books)
3 5 8
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