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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

Comparing the perceptron and the McCulloch-Pitts neuron

In this section, we will cover two mathematical models of biological neurons—the McCulloch-Pitts (MP) neuron and Rosenblatt's perceptron—which create the foundation for neural networks.

The MP neuron

The MP neuron was created in 1943 by Warren McCulloch and Walter Pitts. It was modeled after the biological neuron and is the first mathematical model of a biological neuron. It was created primarily for classification tasks. The MP neuron takes as input binary values and outputs a binary value based on a threshold value. If the sum of the inputs is greater than the threshold, then the neuron outputs 1 (if it is under the threshold, it outputs 0). In the...

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