Preface

Deep Learning (DL) is what humanizes machines. Deep Learning makes it possible for machines to see (through vision models), to listen (through voice devices like Alexa) to talk (through chatbots), to write (through generative models like auto-complete or Q&A) and even be an artist by trying to paint (through style transfer models).

PyTorch Lightning lets researchers build their own DL models quickly & easily without having to worry about the complexities. This book will help you maximize productivity for DL projects while ensuring full flexibility from model formulation to implementation.

The book provides a hands-on approach for implementing PyTorch Lightning DL models and associated methodologies that will have you up and running and productive in no time. 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. Next, you'll build a neural network architecture and deploy an application from scratch and see how you can expand it based on your specific needs, beyond what the framework can provide. The book also demonstrates 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.