All Katas - ML Katas

Implementing a Custom Loss Function with `torch.autograd`

this month medium (<30 mins) | regression loss custom function huber

Create a **custom loss function** that inherits from `torch.nn.Module` and performs a non-standard calculation. For example, a custom Huber loss. This loss is less sensitive to outliers than Mean...

Custom `DataLoader` for On-the-Fly Image Generation

this month medium (<30 mins) | dataset dataloader custom generative synthetic

Create a **custom `torch.utils.data.Dataset`** that doesn't load data from disk. Instead, the `__getitem__` method should **generate** an image on the fly (e.g., a simple geometric shape, a random...

Custom Data Augmentation Pipeline

this month medium (<30 mins) | custom data augmentation transforms pipeline

Create a **custom data augmentation pipeline** using PyTorch's `transforms`. For a given dataset (e.g., a custom image dataset), implement a series of transformations like random rotation,...

Building a Custom `Dataset` and `DataLoader`

this month medium (<30 mins) | dataset dataloader custom data pipeline

Create a **custom `torch.utils.data.Dataset` class** to load a simple, non-image dataset (e.g., from a CSV file). The `__init__` method should read the data, `__len__` should return the total...

Implementing Layer Normalization from Scratch

this month medium (<30 mins) | training custom module normalization layer

Implement **Layer Normalization** as a custom `torch.nn.Module`. Unlike `BatchNorm`, `LayerNorm` normalizes across the features of a single sample, not a batch. Your implementation should...