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270020184
Practical MATLAB Deep Learning: A Project-Based Approach
Item #:
270020184
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Product Details
- 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. 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 predictionWho This Book Is For Engineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.
| Publisher | Apress |
| Publication date | February 7, 2020 |
| Edition | 1st ed. |
| Language | English |
| File size | 22.6 MB |
| Enhanced typesetting | Enabled |
| X-Ray | Not Enabled |
| Word Wise | Not Enabled |
| Print length | 270 pages |
| ISBN-13 | 978-1484251249 |
| Page Flip | Enabled |
| Item Weight | 1 lbs (450 grams) |
Product Description
Practical MATLAB Deep Learning: A Project-Based Approach
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Features & Benefits
- Unlock the potential of deep learning with MATLAB's comprehensive toolboxes.
- Learn through real-world projects including stock market modeling and image-based navigation.
- Implement and train deep learning functions with practical examples.
- Gain insights into dynamics and control while integrating deep-learning algorithms.
- Explore innovative applications like tokamak disruption prediction and ballet classification.
- Ideal for engineers, data scientists, and students eager for hands-on deep learning experience.
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