2. Building Blocks of Neural Networks | The Deep Learning with PyTorch Workshop

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The Deep Learning with PyTorch Workshop

By : Hyatt Saleh , Tim Hoolihan, Learnkart Technology Private Limited , Anuj Shah, Nahar Singh, Subhash Sundaravadivelu

5 (3)

Buy this Book

The Deep Learning with PyTorch Workshop

5 (3)

By: Hyatt Saleh , Tim Hoolihan, Learnkart Technology Private Limited , Anuj Shah, Nahar Singh, Subhash Sundaravadivelu

Buy this Book

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

Introduction

Building a CNN

Data Augmentation

Batch Normalization

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

Introduction

In the previous chapter, it was explained why deep learning has become so popular nowadays, and PyTorch was introduced as one of the most popular libraries for developing deep learning solutions. Although the main syntax for building a neural network using PyTorch was explained, in this chapter, we will further explore the concept of neural networks.

Although neural network theory was developed several decades ago, since the concept evolved from the notion of the perceptron, different architectures have been created to solve different data problems in recent times. This is, in part, due to the different data formats that can be found in real-life data problems, such as text, audio, and images.

The purpose of this chapter is to dive into the topic of neural networks and their main advantages and disadvantages so that you can understand when and how to use them. Then, we will explain the building blocks of the most popular neural network architectures: artificial neural networks (ANNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

Following this, the process of building an effective model will be explained by solving a real-life regression problem. This includes preparing the data to be fed to the neural network (also known as data preprocessing), defining the neural network architecture to be used, and evaluating the performance of the model, with the objective of determining how it can be improved to achieve an optimal solution.

The aforementioned process will be done using one of the neural network architectures that will be discussed in this chapter, all while taking into consideration that the solution for each data problem should be carried out using the architecture that performs best for the data type in question. The other architectures will be used in subsequent chapters to solve more complicated data problems that involve using images and sequences of text as input data.

Note

All the code present in this chapter can be found at: https://packt.live/34MBauE.

The Deep Learning with PyTorch Workshop

By : Hyatt Saleh , Tim Hoolihan, Learnkart Technology Private Limited , Anuj Shah, Nahar Singh, Subhash Sundaravadivelu

The Deep Learning with PyTorch Workshop

By: Hyatt Saleh , Tim Hoolihan, Learnkart Technology Private Limited , Anuj Shah, Nahar Singh, Subhash Sundaravadivelu

Overview of this book

Introduction

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