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The Deep Learning with PyTorch Workshop

You're reading from   The Deep Learning with PyTorch Workshop Build deep neural networks and artificial intelligence applications with PyTorch

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781838989217
Length 330 pages
Edition 1st Edition
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Author (1):
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Hyatt Saleh Hyatt Saleh
Author Profile Icon Hyatt Saleh
Hyatt Saleh
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Toc

Introduction

In the previous chapter, it was explained why deep learning has become so popular nowadays, and PyTorch was introduced as one of the most popular libraries for developing deep learning solutions. Although the main syntax for building a neural network using PyTorch was explained, in this chapter, we will further explore the concept of neural networks.

Although neural network theory was developed several decades ago, since the concept evolved from the notion of the perceptron, different architectures have been created to solve different data problems in recent times. This is, in part, due to the different data formats that can be found in real-life data problems, such as text, audio, and images.

The purpose of this chapter is to dive into the topic of neural networks and their main advantages and disadvantages so that you can understand when and how to use them. Then, we will explain the building blocks of the most popular neural network architectures: artificial...

You have been reading a chapter from
The Deep Learning with PyTorch Workshop
Published in: Jul 2020
Publisher: Packt
ISBN-13: 9781838989217
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