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Hands-On Mathematics for Deep Learning

You're reading from  Hands-On Mathematics for Deep Learning

Product type Book
Published in Jun 2020
Publisher Packt
ISBN-13 9781838647292
Pages 364 pages
Edition 1st Edition
Languages
Author (1):
Jay Dawani Jay Dawani
Profile icon Jay Dawani

Table of Contents (19) Chapters

Preface 1. Section 1: Essential Mathematics for Deep Learning
2. Linear Algebra 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 the concepts in probability

Probability theory is one of the most important fields of mathematics and is essential to the understanding and creation of deep neural networks. We will explore the specifics of this statement in the coming chapters. For now, however, we will focus our effort toward gaining an intricate understanding of this field.

We use probability theory to create an understanding of how likely it is that a certain event will occur. Generally speaking, probability theory is about understanding and dealing with uncertainty.

Classical probability

Let's suppose we have a random variable that maps the results of random experiments to the properties that interest us. The aforementioned random...

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