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

Batch Normalization

It is typical to normalize the input layer in an attempt to speed up learning, as well as to improve performance by rescaling all the features to the same scale. So, the question is, if the model benefits from the normalization of the input layer, why not normalize the output of all the hidden layers in an attempt to improve the training speed even more?

Batch normalization, as its name suggests, normalizes the outputs from the hidden layers so that it reduces the variance from each layer, which is also known as covariance shift. This reduction of the covariance shift is useful as it allows the model to also work well on images that follow a different distribution than the images used to train it.

Take, for instance, a network that has the purpose of detecting whether an animal is a cat. When the network is trained only using images of black cats, batch normalization can help the network also classify new images of cats of different colors by normalizing...

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