Foundations
Training & Optimization
Backpropagation, gradients, and the descent to a good model.
You can jump straight in, but this star assumes Neural Networks. Not recommended as a first stop.
Four layers of depth
Each layer ends with a quiz. Finish layer 4 and you own this concept.
- L1IntuitionWhat 'training a model' actually means: nudging parameters downhill until predictions get good.5m
40 XP - L2MechanicsGradient descent variants, learning rate schedules, and the practical knobs that determine whether training succeeds.8m
70 XP - L3CodeImplementing SGD and Adam from scratch, then writing a full PyTorch training loop.12m
110 XP - L4FoundationsThe convex optimization theory behind gradient descent, convergence guarantees, and why loss landscapes for neural nets are so much harder.15m
180 XP
Where this leads
33 stars in the atlas.