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

Summary

You may be a beginner exploring the field of DL to see whether it's the right career for you. You may be a student of an advanced degree trying to do your research in ML to complete your thesis or get papers published. Or, you may be an expert data scientist with years of experience in training DL models and taking them to production. PyTorch Lightning has something for everyone to do almost anything in DL.

It combines the raw power of PyTorch, which offers efficiency and rigor, with the simplicity of Python, by providing a wrapper over complexity. You can always go as deep as you want in doing some innovative work (as you will see later in this book), while you can also get numerous out-of-the-box neural network architectures that save you from having to reinvent the wheel (which you will also learn about later). It is fully compatible with PyTorch, and code can easily be refactored. It is also perhaps the first framework that is designed for the persona of Data Scientist as opposed to other roles, such as ML researcher, ML-Ops engineer, or data engineer.

We will begin our journey with a simple DL model and will keep expanding our scope to more advanced and complex models with each chapter. You will find that it covers all the famous models, leaving you empowered with Deep Learning skills to make an impact in your organization. So, let's get things moving in our next chapter with your first DL model.