All Katas - ML Katas

Implement a Convolutional Layer

yesterday hard (<30 mins) | cnn jax convolution deep-learning

Implement a 2D convolutional layer from scratch in JAX. This will involve using `jax.lax.conv_general_dilated`. You will need to manage the kernel initialization and the forward pass logic....

Adversarial Training for Robustness

this month hard (>1 hr) | training cnn adversarial robustness fgsm

Implement **adversarial training** on a simple classification model like a small CNN on MNIST. The goal is to make the model robust to adversarial attacks. You'll need to generate adversarial...

Transfer Learning with a Pre-trained Model

this month medium (<1 hr) | cnn transfer learning resnet fine-tuning

Fine-tune a **pre-trained model** (e.g., `resnet18` from `torchvision.models`) on a new, small image classification dataset (e.g., `CIFAR-10`). You'll need to freeze the weights of the initial...

Implement a Siamese Network

this month hard (>1 hr) | pytorch siamese cnn metric learning

Implement a Siamese network for MNIST digit similarity: - Two identical CNNs sharing weights. - Contrastive loss function. - Train on pairs of digits (same/different). Evaluate on test pairs.