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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

Understanding neural Turing machines

The Turing machine (TM) was proposed by Alan Turing in 1936, and it is a mathematical model of computation made up of an infinitely long tape and a head that interacts with the tape by reading, editing, and moving symbols on it. It works by manipulating symbols on the strip according to a predefined set of rules. The tape is made up of an endless number of cells, each of which can contain one of three symbols – 0, 1, or blank (" "). Therefore, this is referred to as a three-symbol Turing machine. Regardless of how simple it seems, it is capable of simulating any computer algorithm, regardless of complexity. The tape that these computations are done on can be considered to be the machine's memory, akin to how our modern-day computers have memory. However, the Turing machine differs from modern-day computers as it has limited...

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