Deep Learning with PyTorch Lightning

Swiftly build high-performance Artificial Intelligence (AI) models using Python

Publisher:Packt Publishing Limited

By: Kunal Sawarkar and Dheeraj Arremsetty

Paid access

|Jun 2024

E-Book €30.99Institutions €130.95

Description

Build, train, and deploy deep learning models quickly and accurately to improve your productivity using PyTorch Lightning Wrapper

Key Features

Become well-versed with PyTorch Lightning and learn how to implement it in various applications
Speed up your research using PyTorch Lightning by creating new loss functions, and architectures
Train and build new DL applications for images, audio, video, structured and unstructured data

Book Description

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.

What you will learn

Customize models that are built for different datasets, model architectures
Understand a variety of DL models from image recognition, NLP to time series
Create advanced DL models to write poems (Semi-Supervised) or create fake images (GAN)
Learn to train on unlabelled images using Self-Supervised Contrastive Learning
Learn to use pre-trained models using transfer learning to save compute
Make use of out-of-the-box SOTA model architectures using Lightning Flash
Explore techniques for model deployment & scoring using ONNX format
Run and tune DL models in a multi-GPU environment using mixed-mode precisions

Who this book is for

If you’re a data scientist curious about deep learning but don't know where to start or feel intimidated by the complexities of large neural networks, then this book is for you. Expert data scientists making the transition from other DL frameworks to PyTorch will also find plenty of useful information in this book, as will researchers interested in using PyTorch Lightning as a reference guide. To get started, you’ll need a solid grasp on Python; the book will teach you the rest