An interactive study of
Artificial intelligence.
Explore the ideas behind learning machines, from a first prediction to a language model.
Machine learning
Data, predictions, loss, and evaluation.
Mathematics for AI
Vectors, probability, derivatives, and optimization.
Neural networks
Layers, gradients, and representation learning.
Language models
Tokens, attention, pretraining, and generation.
Generative AI
Diffusion, fine-tuning, and model alignment.
Retrieval & knowledge
Embeddings, search, and grounded answers.
Agents & safety
Tools, planning, permissions, and feedback.
Production systems
Serving, evaluation, latency, and reliability.
From idea to implementation