PyTorch Pocket Reference - Building and Deploying Deep Learning Models

This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns, and code examples to accelerate your development and reduce the time you spend searching for answers. Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development-from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, Google Cloud, or Azure and deploy your ML models to mobile and edge devices.

• Learn basic PyTorch syntax and design patterns
• Create custom models and data transforms
• Train and deploy models using a GPU and TPU
• Train and test a deep learning classifier
• Accelerate training using optimization and distributed training
• Access useful PyTorch libraries and the PyTorch ecosystem

About the author

Joe Papa has over 25 years experience in research & development and is the founder of INSPIRD.ai. He holds an MSEE and has led AI Research teams with PyTorch at Booz Allen and Perspecta Labs. Joe has mentored hundreds of Data Scientists and has taught 6,000+ students across the world on Udemy.

Product details

• Authors: Joe Papa
• Publisher: O'Reilly
• Languages: English
• Product format: Paperback / Softback
• Released: 31.05.2021
• EAN: 9781492090007
• ISBN: 978-1-4920-9000-7
• Dimensions: 108 mm x 178 mm x 16 mm
• Weight: 290 g
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > Application software; machine learning, Artificial Intelligence, COMPUTERS / Software Development & Engineering / General, BUSINESS & ECONOMICS / Operations Research, COMPUTERS / Programming / General, COMPUTERS / Programming / Algorithms, COMPUTERS / Machine Theory, algorithms and data structures, Enterprise Software, Algorithms & data structures, information architecture, Mathematical theory of computation, Operational research, Computer programming / software engineering, COMPUTERS / Computer Architecture, COMPUTERS / Artificial Intelligence / General, Database design and theory, COMPUTERS / Data Science / Machine Learning, COMPUTERS / Data Science / Data Visualization, Computer Programming / Software Development, COMPUTERS / Database Administration & Management, Data capture and analysis, Data Capture & Analysis, COMPUTERS / Business & Productivity Software / General, Database design & theory