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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

Summary

In this chapter, we learned a lot of concepts. I recommend going through the chapter again if needed because the topics in this chapter are very important to gaining a deep understanding of deep learning. Many of you may be wondering what the chapters you have learned so far have to do with neural networks; we will tie it all together in a couple more chapters.

The next chapter focuses on both convex and non-convex optimization methods and builds the foundation for understanding the optimization algorithms used in training neural networks.

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