Deep Learning / Case study
Fashion-MNIST CNN in PyTorch
Two PyTorch CNN variants for Fashion-MNIST, including a batch-normalized architecture and tracked validation performance.
View repositoryProblem
Build and compare convolutional baselines for ten-class grayscale fashion-image classification.
Solution
Implemented standard and batch-normalized CNNs with torchvision preprocessing, SGD optimization, and per-epoch cost and validation tracking.
Architecture
28×28 image → convolution → batch normalization → max pooling → convolution → batch normalization → max pooling → 10-class linear head.
Technical implementation
Uses Fashion-MNIST’s 70,000 grayscale images across ten clothing categories as part of IBM deep-learning coursework.
Key challenges
Compare a baseline CNN with a batch-normalized variant through a consistent training loop.
Lessons learned
The project makes architectural changes and their training behavior directly inspectable.