The Deep Learning with PyTorch Workshop

By : Hyatt Saleh

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.

Preface

About the Book

1. Introduction to Deep Learning and PyTorch

Introduction

Why Deep Learning?

Introduction to PyTorch

Summary

Free Chapter

2. Building Blocks of Neural Networks

Introduction

Introduction to Neural Networks

Data Preparation

Building a Deep Neural Network

Summary

3. A Classification Problem Using DNN

Introduction

Problem Definition

Dealing with an Underfitted or Overfitted Model

Deploying Your Model

Summary

4. Convolutional Neural Networks

Summary

5. Style Transfer

Introduction

Style Transfer

Implementation of Style Transfer Using the VGG-19 Network Architecture

Summary

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

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Data Augmentation

Learning how to effectively code a neural network is one of the steps involved in developing well-performing solutions. Additionally, to develop great deep learning solutions, it is crucial to find an area of interest in which we can provide a solution to a current challenge. But once all of that is done, we are typically faced with the same issue: getting a dataset of a decent size to get good performance from our models, either by self-gathering or by downloading it from the internet and other available sources.

As you might imagine, and even though it is now possible to gather and store vast amounts of data, this is not an easy task due to the costs associated with it. And so, most of the time, we are stuck working with a dataset containing tens of thousands of entries, and even fewer when referring to images.

This becomes a relevant issue when developing a solution for a computer vision problem, mainly due to two reasons:

The larger the dataset...

The Deep Learning with PyTorch Workshop

By : Hyatt Saleh

The Deep Learning with PyTorch Workshop

By: Hyatt Saleh

Overview of this book

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