All Katas - ML Katas

Implementing a Siamese Network with Triplet Loss

this month hard (>1 hr) | siamese loss learning triplet metric

Building on the previous exercise, let's switch to **Triplet Loss**. This loss function is more powerful as it enforces a margin between an anchor-positive pair and an anchor-negative pair. The...

Implementing Self-Supervised Learning with BYOL

this month hard (>1 hr) | image learning contrastive self-supervised byol

Implement the core logic of **Bootstrap Your Own Latent (BYOL)**. BYOL is a self-supervised learning method that learns image representations without using negative pairs. It consists of two...

Implementing a Custom Learning Rate Scheduler

this month medium (<1 hr) | training custom scheduler learning rate

Implement a **custom learning rate scheduler** that follows a cosine annealing schedule. The learning rate starts high and decreases smoothly to a minimum value, then resets and repeats. Your...

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...