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Hands-On Neural Networks

You're reading from   Hands-On Neural Networks Learn how to build and train your first neural network model using Python

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781788992596
Length 280 pages
Edition 1st Edition
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Authors (2):
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Leonardo De Marchi Leonardo De Marchi
Author Profile Icon Leonardo De Marchi
Leonardo De Marchi
Laura Mitchell Laura Mitchell
Author Profile Icon Laura Mitchell
Laura Mitchell
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: Getting Started FREE CHAPTER
2. Getting Started with Supervised Learning 3. Neural Network Fundamentals 4. Section 2: Deep Learning Applications
5. Convolutional Neural Networks for Image Processing 6. Exploiting Text Embedding 7. Working with RNNs 8. Reusing Neural Networks with Transfer Learning 9. Section 3: Advanced Applications
10. Working with Generative Algorithms 11. Implementing Autoencoders 12. Deep Belief Networks 13. Reinforcement Learning 14. Whats Next? 15. Other Books You May Enjoy

The perceptron

As we anticipated before, the concept of the perceptron is inspired by the biological neuron, and its main function is to decide to block or let a signal pass. Neurons receive a set of binary input, created by electrical signals. If the total signal surpasses a certain threshold, the neuron fires an output.

A perceptron does the same, as we can see in the following diagram:

It can receive multiple pieces of input, and this input is then multiplied by a set of weights. The sum of the weighted signal will then pass through an activation function—in this case, a step function. If the total signal is greater than a certain threshold, the perceptron will either let the signal pass, or not. We can represent this mathematically with the following formula:

This is the mathematical model for a neuron, represented as an explicit sum and as a matrix operation. The...

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