PyTorch Pocket Reference: Building and Deploying Deep Learning Models
| Authors | Papa, Joe |
| Tags | COMPUTERS, Artificial Intelligence, General, Business & Productivity Software, Business Intelligence, computer science, Data Science, Machine theory, Programming, Algorithms, Open Source, Data Modeling & Design, Machine Learning, Mathematics |
| Publisher | O'Reilly |
| Published | 11 mag 2021 |
| Date | 17 mag 2024 |
| Languages | eng |
| Identifiers | isbn: 9781492090007, url: https://github.com/joe-papa/pytorch-book, google: QaMizgEACAAJ, Amazon.com, uri: https://oreil.ly/9781492090007 |
Description
Good complementary intro: Master PyTorch basics with our engaging PyTorch Beginner YouTube tutorial series.
ref:6.453: "If the loss decreases for validation data, then the model is doing well. However, if the training loss decreases but the validation loss does not, then there’s a good chance the model is overfitting."
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.