Build, train, deploy, and scale deep learning models quickly and accurately, improving your productivity using the lightweight PyTorch Wrapper
Key Features
- Become well-versed with PyTorch Lightning architecture and learn how it can be implemented in various industry domains
- Speed up your research using PyTorch Lightning by creating new loss functions, networks, and architectures
- Train and build new algorithms for massive data using distributed training
Book Description
PyTorch Lightning lets researchers build their own Deep Learning (DL) models without having to worry about the boilerplate. With the help of this book, you'll be able to maximize productivity for DL projects while ensuring full flexibility from model formulation through to implementation. You'll take a hands-on approach to implementing PyTorch Lightning models to get up to speed in no time.
You'll start by learning 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 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. As you advance, you'll discover how generative adversarial networks (GANs) work. Finally, you'll work 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 PyTorch book, you'll have developed the knowledge and skills necessary to build and deploy your own scalable DL applications using PyTorch Lightning.
What you will learn
- Customize models that are built for different datasets, model architectures, and optimizers
- Understand how a variety of Deep Learning models from image recognition and time series to GANs, semi-supervised and self-supervised models can be built
- Use out-of-the-box model architectures and pre-trained models using transfer learning
- Run and tune DL models in a multi-GPU environment using mixed-mode precisions
- Explore techniques for model scoring on massive workloads
- Discover troubleshooting techniques while debugging DL models
Who this book is for
This deep learning book is for citizen data scientists and expert data scientists transitioning from other frameworks to PyTorch Lightning. This book will also be useful for deep learning researchers who are just getting started with coding for deep learning models using PyTorch Lightning. Working knowledge of Python programming and an intermediate-level understanding of statistics and deep learning fundamentals is expected.
Table of Contents
- PyTorch Lightning Adventure
- Getting Off the Ground with Your First Deep Learning Model
- Transfer Learning Using Pre-Trained Models
- Ready-to- Use Models from Bolts
- Time Series Models
- Deep Generative Models
- Semi-Supervised Learning
- Self-Supervised Learning
- Deploying and Scoring Models
- Scaling and Managing Training
Kunal Sawarkar is a Distinguished Engineer for Generative AI and Chief AI Scientist at IBM. He runs Applied AI Build Labs for crafting and building new AI products. He also serves as an Angel Investor and as an Advisory Board Member. He holds a Master's degree from Harvard University with major coursework in Applied Statistics. He has been applying Machine Learning to solve unsolved problems in industry & society, with a special focus on Deep Learning. He has built & led various Product labs in the past and holds 21+ patents & papers publications in this field. When not diving into data, he enjoys rock climbing & learning to fly apart from continuing his incessant curiosity in astronomy & wildlife.
"What attracted me to Statistics is the poetry of chance & certainty. Crafting AI is like creating a symphony out of chaos!"
