Book Image

The Deep Learning with PyTorch Workshop

By : Hyatt Saleh
Book Image

The Deep Learning with PyTorch Workshop

By: Hyatt Saleh

Overview of this book

Want to get to grips with one of the most popular machine learning libraries for deep learning? The Deep Learning with PyTorch Workshop will help you do just that, jumpstarting your knowledge of using PyTorch for deep learning even if you’re starting from scratch. It’s no surprise that deep learning’s popularity has risen steeply in the past few years, thanks to intelligent applications such as self-driving vehicles, chatbots, and voice-activated assistants that are making our lives easier. This book will take you inside the world of deep learning, where you’ll use PyTorch to understand the complexity of neural network architectures. The Deep Learning with PyTorch Workshop starts with an introduction to deep learning and its applications. You’ll explore the syntax of PyTorch and learn how to define a network architecture and train a model. Next, you’ll learn about three main neural network architectures - convolutional, artificial, and recurrent - and even solve real-world data problems using these networks. Later chapters will show you how to create a style transfer model to develop a new image from two images, before finally taking you through how RNNs store memory to solve key data issues. By the end of this book, you’ll have mastered the essential concepts, tools, and libraries of PyTorch to develop your own deep neural networks and intelligent apps.

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Table of Contents (8 chapters)
Preface
About the Book
1
1. Introduction to Deep Learning and PyTorch
1. Introduction to Deep Learning and PyTorch
Introduction
Why Deep Learning?
Introduction to PyTorch
Summary
Free Chapter
2
2. Building Blocks of Neural Networks
2. Building Blocks of Neural Networks
Introduction
Introduction to Neural Networks
Data Preparation
Building a Deep Neural Network
Summary
3
3. A Classification Problem Using DNN
3. A Classification Problem Using DNN
Introduction
Problem Definition
Dealing with an Underfitted or Overfitted Model
Deploying Your Model
Summary
4
4. Convolutional Neural Networks
4. Convolutional Neural Networks
Introduction
Building a CNN
Data Augmentation
Batch Normalization
Summary
5
5. Style Transfer
5. Style Transfer
Introduction
Style Transfer
Implementation of Style Transfer Using the VGG-19 Network Architecture
Summary
6
6. Analyzing the Sequence of Data with RNNs
6. Analyzing the Sequence of Data with RNNs
Introduction
Recurrent Neural Networks
Long Short-Term Memory Networks
LSTM Networks in PyTorch
Natural Language Processing
Sentiment Analysis in PyTorch
Summary
Appendix
1. Introduction to Deep Learning and PyTorch
2. Building Blocks of Neural Networks
3. A Classification Problem Using DNNs
4. Convolutional Neural Networks
5. Style Transfer
6. Analyzing the Sequence of Data with RNNs

Sentiment Analysis in PyTorch

Building a model to perform sentiment analysis in PyTorch is fairly similar to what we have seen so far with RNNs. The difference is that, on this occasion, the text data will be processed word by word. The steps that are required to build such a model will be provided in this section.

Preprocessing the Input Data

As with any other data problem, you need to load the data into the code, bearing in mind that different methodologies are used for different data types. Besides converting the entire set of words into lowercase, the data undergoes some basic transformations that will allow you to feed the data into the network. The most common transformations are as follows:

  • Eliminating punctuation: When processing text data word by word for NLP purposes, remove any punctuation. This is done to avoid taking the same word as two separate words because one of them is followed by a period, comma, or any other special character. Once this has been achieved...
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