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

LSTM Networks in PyTorch

The process of defining the LSTM network architecture in PyTorch is similar to that of any other neural network that we have discussed so far. However, it is important to note that, when dealing with sequences of data that are different from those of numbers, there is some preprocessing required in order to feed the network with data that it can understand and process.

Considering this, we need to explain the general steps for training a model to be able to take text data as inputs and retrieve a new piece of textual data. It is important to mention that not all the steps explained here are strictly required, but as a group, they make clean and reusable code for using LSTMs with textual data.

Preprocessing the Input Data

The first step is to load the text file into the code. This data will go through a series of transformations in order to be fed into the model properly. This is necessary because neural networks perform a series of mathematical computations...

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