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Hands-On Mathematics for Deep Learning

You're reading from   Hands-On Mathematics for Deep Learning Build a solid mathematical foundation for training efficient deep neural networks

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Product type Paperback
Published in Jun 2020
Publisher Packt
ISBN-13 9781838647292
Length 364 pages
Edition 1st Edition
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Author (1):
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Jay Dawani Jay Dawani
Author Profile Icon Jay Dawani
Jay Dawani
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Essential Mathematics for Deep Learning
2. Linear Algebra FREE CHAPTER 3. Vector Calculus 4. Probability and Statistics 5. Optimization 6. Graph Theory 7. Section 2: Essential Neural Networks
8. Linear Neural Networks 9. Feedforward Neural Networks 10. Regularization 11. Convolutional Neural Networks 12. Recurrent Neural Networks 13. Section 3: Advanced Deep Learning Concepts Simplified
14. Attention Mechanisms 15. Generative Models 16. Transfer and Meta Learning 17. Geometric Deep Learning 18. Other Books You May Enjoy

Meta learning

Meta learning—also known as learning to learn—is another fascinating topic within deep learning and is considered by many to be a promising path toward Artificial General Intelligence (AGI). For those of you who do not know what AGI is, it is when artificial intelligence reaches the capacity to understand and learn to do any type of intelligent task that a human is capable of doing, which is the goal of artificial intelligence.

Deep neural networks, as we know, are very data-hungry and require a lot of training time (depending on the size of the model), which can sometimes be several weeks, whereas humans are able to learn new concepts and skills a lot faster and more efficiently. For example, as kids, we can quickly learn to tell the difference between a donkey, a horse, and a zebra with absolute certainty after only seeing them once or a handful of...

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