Title
Practical Matlab Deep Learning: A Projectbased Approach
Delivery time: 8-12 business days (International)
Harness The Power Of Matlab For Deeplearning Challenges. This Book Provides An Introduction To Deep Learning And Using Matlab'S Deeplearning 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 Anglesonly Orbit Determination. You'Ll Cover Dynamics And Control, And Integrate Deeplearning 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.
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Shipping & Returns
Shipping
We ship your order within 2–3 business days for USA deliveries and 5–8 business days for international shipments. Once your package has been dispatched from our warehouse, you'll receive an email confirmation with a tracking number, allowing you to track the status of your delivery.
Returns
To facilitate a smooth return process, a Return Authorization (RA) Number is required for all returns. Returns without a valid RA number will be declined and may incur additional fees. You can request an RA number within 15 days of the original delivery date. For more details, please refer to our Return & Refund Policy page.
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Warranty
We provide a 2-year limited warranty, from the date of purchase for all our products.
If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.
This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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Frequently Asked Questions
- Q: What is the main focus of 'Practical MATLAB Deep Learning'? A: The book focuses on harnessing MATLAB for deep learning challenges, providing an introduction to deep learning concepts and demonstrating how to use MATLAB's deep-learning toolboxes effectively.
- Q: Who is the intended audience for this book? A: This book is intended for engineers, data scientists, and students who are looking for a resource rich in examples on deep learning using MATLAB.
- Q: What key topics are covered in the book? A: Key topics include modeling complex systems, dynamics and control, deep-learning algorithms, aircraft navigation using images, and classification tasks such as ballet pirouette analysis.
- Q: How does this book help with understanding deep learning algorithms? A: The book provides practical examples and comparisons of deep learning algorithms using MATLAB, helping readers to write and train their own deep learning functions.
- Q: What is the length and binding type of the book? A: The book has 258 pages and is available in a paperback binding.
- Q: When was 'Practical MATLAB Deep Learning' published? A: The book was published on January 4, 2020.
- Q: Does the book include hands-on projects? A: Yes, the book adopts a project-based approach, allowing readers to engage in hands-on projects related to deep learning using MATLAB.
- Q: Is there any prerequisite knowledge required to read this book? A: While the book is designed for those familiar with MATLAB, a basic understanding of deep learning concepts would be beneficial for better comprehension.
- Q: What practical applications does the book cover? A: The book covers practical applications such as tokamak disruption prediction, aircraft navigation, and sensor data analysis for ballet pirouette classification.
- Q: Who is the author of this book? A: The book is authored by Michael Paluszek.