Slides are the complete handouts; Present opens the classroom build, and each Cheatsheet is a two-page review guide.
Resource key: ▤ handout · ▶ classroom presentation · ★ cheatsheet · ⌨ notebook · ◉ recording
Foundations
| 1 |
Where Deep Learning Losses Come From I — distributions, likelihood, and loss |
▤ Slides · ▶ Present · ★ Cheatsheet |
| 2 |
Where Deep Learning Losses Come From II — MLE, regression, and classification |
▤ Slides · ▶ Present · ★ Cheatsheet |
| 3 |
Likelihood to Loss in Practice — MAP, regularization, and robust observation models |
▤ Slides · ★ Cheatsheet · ⌨ Likelihood notebook · ⌨ Robust regression |
| 4 |
From Linear Models to Neural Networks — neurons, activations, multilayer networks, XOR, and approximation |
▤ Slides · ▶ Present · ★ Cheatsheet · ⌨ Linear GD · ⌨ XOR |
Gradients & optimization
| 5 |
Optimization I — Gradient Estimates & Step Sizes — batches, stochastic gradients, and optimizer geometry |
▤ Slides · ▶ Present · ★ Cheatsheet · ◉ Recording |
| 6 |
Backpropagation & Autograd from Scratch — computation graphs, reverse mode, and a scalar engine |
▤ Slides · ▶ Present · ★ Cheatsheet · ⌨ Colab · ◉ Recording |
| 7 |
Optimization II — Momentum, RMSProp & Adam — moving averages and adaptive coordinate scaling |
▤ Slides · ▶ Present · ★ Cheatsheet · ◉ Momentum · ◉ RMSProp & Adam |
| 8 |
Vector, Matrix & Affine Autograd — gradients, Jacobians, dense layers, and batched VJPs |
▤ Calculus · ▤ Autograd · ★ Cheatsheet · ⌨ Colab |
| 9 |
Valid Outputs by Design & Learning-Rate Schedules — constrained parameters, warmup, decay, and cosine |
▤ Valid outputs · ▤ LR schedules · ★ Cheatsheet |