Eai meu chapa, eu sou o Pablo [ou como minha rapaziada me chama... Pablin]
Tô tentando sobreviver no mestrado em Computação aplicada a inteligencia artificial, sou meio que um nerdzão que curte Machine Learning, IA, modelos quantizados e modelos rodando na minha infra, principalmente SLMs(fuck big techs... Caô, óbvio que usei tio claudinho e o primo geminius pra criar o blog uahsuahsuahu). Esse blog é onde eu documento meus estudos(eu e meus modelos locais ou não), com metáfora e tudo, mas sem fugir dos termos técnicos. O lance aqui é tentar fazer você pegar alguma informação e sentir que vai conseguir aplicar isso no seu projeto ou estudo(assim como eu tô tentando também).
Latest posts
See all- Adaline: Train on the Line, Classify on the Sign
2026-08-20
Lectures 2e and 2f: the professor trains Adaline by fitting a continuous line to the ±1 labels (instead of fitting directly on the sign), and I connect this to the delta rule that already showed up in the other playlist. In the end, a deliberately biased dataset reveals the problem was never the training algorithm.
- Swapping the Cost Function Like Changing Clothes
2026-08-20
Lectures 3a and 3b: the professor generalizes training to accept any pluggable cost function, and each choice (Widrow-Hoff, perceptron criterion, log-likelihood, hinge) recovers a different algorithm from this playlist. Along the way, I found a real mismatch between activation function and label encoding.
- Perceptron: the First Neuron That Learns on Its Own
2026-08-20
Lecture 1a: the professor implements the Perceptron Learning Algorithm from scratch, no bias yet. I tell the story of two papers that came before it and find a real bug hiding in the algorithm's stopping condition.
Timeline
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Getting shaken up by Go2026-08-19
Playlists
- Machine Learning Specialization (Andrew Ng)
My step-by-step notes going through Andrew Ng's Machine Learning Specialization (DeepLearning.AI / Stanford), course by course, lab by lab.
- Pattern Recognition
My notes from my Pattern Recognition course, lecture by lecture, with Bishop's book as the theoretical backbone.
- Neural Networks
My notes from my Neural Networks course, lecture by lecture, with Aggarwal's book as the theoretical backbone. A living playlist, growing along with the course.
- Papers
A diagonal read of articles and surveys I found cool, with no pretense of turning into a lecture. Simple code, a chart to give you a view, and the same conversation as always with you.