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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

Product type Paperback

Published in Jun 2020

Publisher Packt

ISBN-13 9781838647292

Length 364 pages

Edition 1st Edition

Languages

Python

Tools

Pandas

Concepts

Deep Learning

Author (1):

Jay Dawani

View More author details

Table of Contents (19) Chapters

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

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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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Authors (1)

Dawani

Dawani

Jay Dawani is a former professional swimmer turned mathematician and computer scientist. He is also a Forbes 30 Under 30 Fellow. At present, he is the Director of Artificial Intelligence at Geometric Energy Corporation (NATO CAGE) and the CEO of Lemurian Labs - a startup he founded that is developing the next generation of autonomy, intelligent process automation, and driver intelligence. Previously he has also been the technology and R&D advisor to Spacebit Capital. He has spent the last three years researching at the frontiers of AI with a focus on reinforcement learning, open-ended learning, deep learning, quantum machine learning, human-machine interaction, multi-agent and complex systems, and artificial general intelligence.

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