Handouts
Full class walkthroughs as single-page documents. Each one renders every figure from real training runs, ships the code to reproduce it, and includes solved checkpoints.
They complement the class pages rather than replace them: each goes deep on one topic and works both as a companion during class and as a reference afterwards.
Written in Portuguese
The handouts are course material for the in-person classes and are written in Brazilian Portuguese. The code, the library names and the figures are language-independent.
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Training an MLP: batch, optimizer and learning rate
MNIST — 70 000 handwritten digits, one fixed architecture: 784 → 128 → 64 → 10.
The three decisions that are neither the architecture nor the update rule. Full-batch, mini-batch and pure SGD measured against each other by epoch, by weight update and by wall clock; gradient noise measured directly against the 1/√B prediction; the seven optimizers compared first at one shared learning rate and then each at its own; a 6 × 6 batch × learning-rate map and a 7 × 9 optimizer × learning-rate grid, both explorable in the page; and what a loss curve has to look like before it means anything.
~2 h · 10 figures · 2 live simulators · 7 checkpoints with solutions
Classroom use
Each handout has an anchored sidebar index, a copy button on every code block, and its own print stylesheet — the menu, buttons and interactive elements are dropped when printing or exporting to PDF.