This skill introduces the PyTorch framework for developing and training machine learning and deep learning models. It emphasizes model definition, training loops, and debugging in a dynamic computation graph environment.

• External: Programming experience with Python
• Internal: BDA5.2.1 Basics of Neural Networks (recommended)

• Define and train neural networks using the PyTorch nn.Module interface.
• Implement forward passes, loss computation, and backward propagation.
• Use DataLoader and Dataset classes to efficiently handle input data.
• Apply GPU acceleration and mixed precision with PyTorch utilities.
• Debug models interactively using PyTorch’s dynamic computation graph.

Caution: All text is AI generated