Sawarkar / Arremsetty Deep Learning with PyTorch Lightning

Swiftly build high-performance Artificial Intelligence (AI) models using Python
1. Auflage 2024
ISBN: 978-1-80056-927-0
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 366 Seiten

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

Autoren/Hrsg.

Sawarkar, Kunal

Arremsetty, Dheeraj

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

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

Über Autor(innen)

Sawarkar Kunal :

Kunal Sawarkar is a chief data scientist and AI thought leader. He leads the worldwide partner ecosystem in building innovative AI products. He also serves as an advisory board member and an angel investor. 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 and self-supervised learning. Kunal has led various AI product R&D labs and has 20+ patents and papers published in this field. When not diving into data, he loves doing rock climbing and learning to fly aircraft, in addition to an insatiable curiosity for astronomy and wildlife.