Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development a 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
Download PyTorch Pocket Reference: Building and Deploying Deep Learning Models PDF or ePUB format free