Deep Learning with PyTorch, Second Edition

Luca Antiga is a deep learning researcher and entrepreneur known for translating theory into high-impact AI applications. With extensive industry and academic collaborations, Luca brings clarity and pragmatic rigor to every page. He distills years of neural-network innovation into guidance that accelerates reader competence.  Eli Stevens is a seasoned machine-learning engineer recognized for simplifying complex architectures for production teams. With startup intensity and open-source spirit, Eli delivers frank, actionable insights throughout the book. He converts frontier research into approachable steps that help readers deploy real-world solutions.  Howard Huang is a software engineer on the core PyTorch team, known for advancing distributed training at scale. With insider knowledge of the framework, Howard injects authoritative best practices into the narrative. He turns deep infrastructure expertise into clear tactics that boost reader productivity.  Thomas Viehmann is a data-science consultant and educator praised for demystifying advanced AI concepts. With classroom experience and community mentorship, Thomas offers an encouraging, structured teaching style. He translates academic depth into tools and patterns readers can apply immediately.

This book is an exceptional resource for anyone looking to dive into deep learning with the PyTorch framework. It masterfully introduces complex concepts in a way that is approachable for beginners and students, offering clear explanations and practical examples that make learning both engaging and effective. Raja Rao Budaraju, Senior Member of Technical Staff, Oracle  If you're looking for a way to quickly get up to speed in deep learning using PyTorch, I'd highly recommend this book. It'll provide you with a really solid base to expand and experiment with many different projects using PyTorch. Jordan Samek, Data Analyst | Data Scientist

• PART 1: CORE PYTORCH 1 INTRODUCING DEEP LEARNING AND THE PYTORCH LIBRARY 2 PRETRAINED NETWORKS 3 IT STARTS WITH A TENSOR 4 REAL-WORLD DATA REPRESENTATION USING TENSORS  5 THE MECHANICS OF LEARNING 6 USING A NEURAL NETWORK TO FIT THE DATA 7 TELLING BIRDS FROM AIRPLANES: LEARNING FROM IMAGES 8 USING CONVOLUTIONS TO GENERALIZE PART 2: PRACTICAL APPLICATIONS 9 HOW TRANSFORMERS WORK 10 DIFFUSION MODELS FOR IMAGES 11 USING PYTORCH TO FIGHT CANCER 12 COMBINING DATA SOURCES INTO A UNIFIED DATASET 13 TRAINING A CLASSIFICATION MODEL TO DETECT SUSPECTED TUMORS 14 IMPROVING TRAINING WITH METRICS AND AUGMENTATION 15 USING SEGMENTATION TO FIND SUSPECTED NODULES 16 TRAINING MODELS ON MULTIPLE GPUS 17 DEPLOYING TO PRODUCTION