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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

Neural Network Fundamentals

Artificial neural networks (ANNs) are a set of bio-inspired algorithms. In particular, they are loosely inspired by biological brains; exactly like animal brains, ANNs consist of simple units (neurons) connected to each other. In biology, these units are called neurons. They receive, process, and transmit a signal to other neurons, acting like a switch.

The elements of a neural network are quite simple on their own; the complexity and the power of these systems come from the interaction between the elements. A human brain has more than 100 billion neurons and 100 trillion connections.

In the previous chapter, we introduced a supervised learning problem. In this chapter, we will cover the main building blocks to create Neural Networks (NNs) to solve such a problem. We will cover all of the elements to create a feedforward neural network, and we&apos...

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