PyTorch Neural Networks in Notebooks ML Studio
Watch the video below for an overview of how to create, train, and evaluate neural networks using PyTorch within Azure ML's notebook environment:
Note:
The notebook demonstrated in this video is fully annotated and can be completed without the video tutorial if that works best for you.
What You'll Learn
- Set up a PyTorch environment: Verify PyTorch installation and configure the development environment within Azure ML Studio
- Data preprocessing for deep learning: Load datasets, perform feature scaling, and convert data to PyTorch tensors
- Neural network architecture: Design and implement a feedforward neural network for regression tasks
- Training procedures: Configure loss functions, optimisers, and implement training loops with validation
- Model evaluation: Assess model performance using appropriate metrics and visualisations
- Model persistence: Save and load trained models for future use
💡 Tip for the future
This tutorial uses the free subscription compute tier, which runs on CPU rather than GPU. For larger models or datasets requiring GPU acceleration, consider upgrading your subscription through your institution's Azure tenant.