1 · Machine–Learning Model Taxonomy 🔗1.1 Discriminative vs Generative 🔗 Discriminative models learn the conditional distribution $P(y\mid x)$ to separate classes. Primary use · classification/regression. Typical nature · often deterministic at inference. Examples · Logistic Regression, SVM, plain Feed‑forward NNs, ResNet‑like CNNs. Generative models learn the joint distribution $P(x,y)=P(x\mid y)P(y)$ and can synthesize new x. Primary use · both classification and data generation/simulation. Typical nature · probabilistic/stochastic. Examples · Naïve Bayes, Hidden Markov Models, GANs, VAEs, Diffusion models.

Quick Announcement 🔗I started this blog to reflect on my learning and write down my ideas in a more digestible format. I kind of deviated from that by making it very textbook-style, code-centric articles. They were also extremely long, so—based on feedback—I’d like to start over and do some mix-and-match. While I’ll still occasionally write code-heavy articles, I’ll also do these small “TILs” to jot down raw notes in a more standard format.