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

Introduction to Neural Networks

Neural networks learn from training data, rather than being programmed to solve a particular task by following a set of rules. This learning process can follow one of the following methodologies:

  • Supervised learning: This is the simplest form of learning as it consists of a labeled dataset, where the neural network finds patterns that explain the relationship between the features and the target. The iterations during the learning process aim to minimize the difference between the predicted value and the ground truth. One example of this is classifying a plant based on the attributes of its leaves.
  • Unsupervised learning: In contrast to the preceding methodology, unsupervised learning consists of training a model with unlabeled data (meaning that there is no target value). The purpose of this is to arrive at a better understanding of the input data. In general, networks take input data, encode it, and then reconstruct the content from the encoded...
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