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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

Probability and Statistics

In this chapter, we will cover two of the most important areas of mathematics—probability and statistics. These are two terms that you've likely come across a number of times in your everyday life. People use it to justify just about everything that occurs or when they're trying to prove a point. Once you are done with this chapter, you will have a firm grasp of both of them and will understand how they both are related and how they differ.

This chapter will cover the following topics:

  • Understanding the concepts in probability
  • Essential concepts in statistics
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