In this module, you’ll move from regression to image classification, tackling the challenges of working with image data. You’ll learn to manage datasets with PyTorch’s transforms, Dataset, and DataLoader, and to build models beyond Sequential using nn.Module. Along the way, you’ll see how networks learn through loss functions, gradients, and optimization, apply GPU acceleration, and put it all together by training classifiers for digits and letters end to end.

This module tackles real-world data challenges with the Oxford Flowers dataset, showing how poor pipelines can break even the best models. You’ll learn to build custom Datasets, implement transform pipelines, split data correctly, and apply production-ready practices like error handling, augmentation, and monitoring to create a reliable workflow.

In this module, you’ll explore Convolutional Neural Networks (CNNs), learning how filters detect patterns like edges and textures, pooling reduces dimensions, and these components combine into full architectures. You’ll see how PyTorch’s dynamic graphs let you choose between quick Sequential models and flexible custom modules. By the end, you’ll build CNNs with dropout, weight decay, and inspection tools to debug shape mismatches and understand parameters.