Deep Learning with PyTorch Lightning : Swiftly build high-performance Artificial Intelligence (AI) models using Python

Kunal Sawarkar

ISBN 10: 180056161X / ISBN 13: 9781800561618

Published by Packt Publishing, 2022

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Build, train, deploy, and scale deep learning models quickly and accurately, improving your productivity using the lightweight PyTorch WrapperKey Features:Become well-versed with PyTorch Lightning architecture and learn how it can be implemented in various industry domainsSpeed up your research using PyTorch Lightning by creating new loss functions, networks, and architecturesTrain and build new algorithms for massive data using distributed trainingBook 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 optimizersUnderstand how a variety of Deep Learning models from image recognition and time series to GANs, semi-supervised and self-supervised models can be builtUse out-of-the-box model architectures and pre-trained models using transfer learningRun and tune DL models in a multi-GPU environment using mixed-mode precisionsExplore techniques for model scoring on massive workloadsDiscover troubleshooting techniques while debugging DL modelsWho 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. Seller Inventory # 9781800561618

Bibliographic Details

Title: Deep Learning with PyTorch Lightning : ...

Publisher: Packt Publishing

Publication Date: 2022

Binding: Taschenbuch

Condition: Neu

About this title

Synopsis:

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

1. PyTorch Lightning Adventure
2. Getting Off the Ground with Your First Deep Learning Model
3. Transfer Learning Using Pre-Trained Models
4. Ready-to- Use Models from Bolts
5. Time Series Models
6. Deep Generative Models
7. Semi-Supervised Learning
8. Self-Supervised Learning
9. Deploying and Scoring Models
10. Scaling and Managing Training

About the Author:

Kunal Sawarkar is a Chief Data Scientist and AI thought leader. He leads the worldwide AI Build Labs responsible for crafting innovative products. He also serves as an Angel Invenstor with focus on sustainbility via AI 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 previously unsolved problems in industry and society, with a special focus on Deep Learning. Kunal has led various product labs over the years and has 11+ patents and papers published in this field. When not diving into data, he enjoys rock climbing and learning to fly, in addition to an insatiable curiosity for astronomy and wildlife.