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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

Product type Paperback

Published in Jul 2020

Publisher Packt

ISBN-13 9781838989217

Length 330 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Artificial Intelligence

Author (1):

Hyatt Saleh

View More author details

Table of Contents (8) Chapters

Preface

1. Introduction to Deep Learning and PyTorch

2. Building Blocks of Neural Networks FREE CHAPTER

3. A Classification Problem Using DNN

4. Convolutional Neural Networks

5. Style Transfer

6. Analyzing the Sequence of Data with RNNs

Appendix

3. A Classification Problem Using DNN

Overview

In this chapter, we'll look at a real-life example in the banking industry in order to solve a classification data problem. You will learn how to make use of PyTorch's custom modules to define the network architecture and train the model. You will also explore the concept of error analysis to improve a model's performance. Finally, you will study the different approaches to deploying a model in order to make use of it in the future. By the end of this chapter, you will have a firm understanding of this process so that you can develop an end-to-end solution to a classification data problem using a deep neural network (DNN) in PyTorch.

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Authors (1)

Hyatt Saleh

Hyatt Saleh

Hyatt Saleh discovered the importance of data analysis for understanding and solving real-life problems after graduating from college as a business administrator. Since then, as a self-taught person, she not only works as a machine learning freelancer for many companies globally, but has also founded an artificial intelligence company that aims to optimize everyday processes. She has also authored Machine Learning Fundamentals, by Packt Publishing.

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