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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

6. Analyzing the Sequence of Data with RNNs

Overview

This chapter expands on the concept of recurrent neural networks. You will learn about the learning process of Recurrent Neural Networks (RNNs) and how they store memory. The chapter will introduce the Long Short-Term Memory (LSTM) network architecture, which uses short- and long-term memory to solve data problems using sequences of data. By the end of this chapter, you will have a firm grasp of RNNs and of how to solve Natural Language Processing (NLP) data problems.

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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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