PyTorch Foundations for Modern Vision

Use tensor operations and automatic differentiation to compute gradients for batched image data.

Normalize, resize, and augment image batches with a composable preprocessing pipeline.

Create a custom PyTorch Dataset and DataLoader for an annotated vision dataset.

Implement a reusable training and validation loop with checkpointing.

Select an appropriate loss function and optimizer for a supervised vision task.

Diagnose underfitting, overfitting, and data-pipeline faults from training curves and sample predictions.

Make a vision experiment reproducible by controlling seeds, configurations, and dependency versions.