Book Image

Deep Learning with PyTorch Lightning

By : Kunal Sawarkar
3.5 (2)
Book Image

Deep Learning with PyTorch Lightning

3.5 (2)
By: Kunal Sawarkar

Overview of this book

Building and implementing deep learning (DL) is becoming a key skill for those who want to be at the forefront of progress.But with so much information and complex study materials out there, getting started with DL can feel quite overwhelming. Written by an AI thought leader, Deep Learning with PyTorch Lightning helps researchers build their first DL models quickly and easily without getting stuck on the complexities. With its help, you’ll be able to maximize productivity for DL projects while ensuring full flexibility – from model formulation to implementation. Throughout this book, you’ll learn how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. You’ll build a neural network architecture, deploy an application from scratch, and see how you can expand it based on your specific needs, beyond what the framework can provide. In the later chapters, you’ll also learn how to implement capabilities to build and train various models like Convolutional Neural Nets (CNN), Natural Language Processing (NLP), Time Series, Self-Supervised Learning, Semi-Supervised Learning, Generative Adversarial Network (GAN) using PyTorch Lightning. By the end of this book, you’ll be able to build and deploy DL models with confidence.
Table of Contents (15 chapters)
1
Section 1: Kickstarting with PyTorch Lightning
6
Section 2: Solving using PyTorch Lightning
11
Section 3: Advanced Topics

Getting started with GAN models

One of the most amazing applications of GANs is generation. Just look at the following picture of a girl; can you guess whether she is real or simply generated by a machine?

Figure 6.1 – Fake face generation using StyleGAN (image credit – https://thispersondoesnotexist.com)

Creating such incredibly realistic faces is one of the most successful use cases of GANs. However, GANs are not limited to just generating pretty faces or deepfake videos; they also have key commercial applications as well, such as generating images of houses or creating new models of cars or paintings.

While generative models have been used in the past in statistics, deep generative models such as GANs are relatively new. Deep generative models also include Variational Autoencoders (VAEs) and auto-regressive models. However, with GAN being the most popular method, we will focus on them here.

What is a GAN?

Interestingly, GAN originated...