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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

Linear Neural Networks

In this chapter, we will go over some of the concepts in machine learning. It is expected that you have previously studied and have an understanding of machine learning. So this chapter will serve as a refresher for some of the concepts that will be needed throughout this book, rather than a comprehensive study of all the machine learning approaches.

In this chapter, we will focus on linear neural networks, which are the simplest type of neural networks and are used for tasks such as linear regression, polynomial regression, logistic regression, and softmax regression, which are used most frequently in statistical learning.

We use regression to explain the relationship between one or more independent variables and a dependent variable. The concepts we will learn in this chapter are crucial for furthering our understanding of how machine learning works before...

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