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Implement ML Papers From Scratch

Each track breaks one landmark paper into small steps. You implement every step in Python in the browser, and tests check your code before you move on. If you are new, start with the Transformer, or read which ML papers to implement from scratch to choose a track.

Transformers and LLMs

Convolutional networks

Sequence models and embeddings

Generative models

Generative Adversarial Networks (GAN)

Generative Adversarial Networks (GAN) badge
1

Generator

Create fake data from noise

Easy
2

Discriminator

Classify real vs fake

Medium
3

GAN Loss Functions

Adversarial objectives

Medium
4

Training Loop

Alternating optimization

Hard
5

Mode Collapse

Detect diversity issues

Easy
6

Complete GAN

Full adversarial system

Hard
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