Build AI systems from first principles — not just use them.
A hands-on track for builders who want to understand how AI systems actually work under the hood. Start from tensors and autograd, build a GPT from scratch, and ship a fine-tuned model behind your own streaming API.
Most tutorials stop at model.fit(). You keep going — to a live service, real traffic, and alerts when the model drifts.
Go from scikit-learn scripts to shipping retrieval, agents, and evaluated language-model endpoints that survive real traffic.
Stop treating the model as a black box: 100 challenges take you from a single gradient to a fine-tuned model you can query.
API bills scale with your success and vendors deprecate models under you — this is the skill set that makes both someone else's problem.
Cut the per-token bill and the vendor lock-in: teach an open model your task on one free GPU, then serve it from hardware you control.
Every feed, search box and ad slot runs the same funnel — build yours end to end and prove with real numbers that it beats popularity.
Most training jobs leave half the hardware idle — learn to measure where the time and the money actually go, then take them back.
A pretrained model can autocomplete anything and follow nothing — closing that gap is the difference between a demo and a product.