Deep Learning with PyTorch Lightning

By Kunal Sawarkar

Early Access

This is an Early Access product. Early Access chapters haven’t received a final polish from our editors yet. Every effort has been made in the preparation of these chapters to ensure the accuracy of the information presented. However, the content in this book will evolve and be updated during the development process.

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About this book

PyTorch Lightning lets researchers build their own deep learning (DL) models without having to worry about the boilerplate. 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 to implementing PyTorch Lightning 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 network and application from scratch and see how you can expand it based on your own specific needs, beyond what the framework can provide. The book also demonstrates how to implement out-of-box capabilities to build and train self-supervised learning, semi-supervised learning, and time series models using PyTorch Lightning. Later, you will gain detailed insights into how generative adversarial networks (GANs) work. Finally, you will get to grips with deployment-ready applications, focusing on faster performance and scaling, model scoring on massive volumes of data, and model debugging.

By the end of this book, you will be able to build and deploy your own scalable DL applications using PyTorch Lightning.

Publication date:
January 2022

About the Author

  • Kunal Sawarkar

    Kunal Sawarkar is a principal data scientist at IBM. He is responsible for transforming people, processes, and products that impact companies through analytics and empowerment with data. He provides end-end expertise on the AI-Ops lifecycle; from data engineering to modeling to architecture to deployment to post-deployment monitoring and explainability. He has built eminence for Watson AI products by solving avant-garde ML problems that demand innovation & research. He is a speaker at various technical conferences’ like Data & AI Forum, Think, Software Universe, Ignite, etc. He is very passionate to utilize AI for wildlife and environment conservation.

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Deep Learning with PyTorch Lightning
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