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Series
This is a series specifically for anyone trying to break down ML and doesn't want to go through tutorial hell.
Machine Learning often feels intimidating. Neural networks. Transformers. LLMs. Diffusion models. But behind all of them lies something surprisingly simple: Optimization. And the best place to underst
In the previous article, we explored Simple Linear Regression, where a model used one feature to predict an output. But real-world problems rarely depend on a single variable. For example, the price o
When we want to predict a binary outcome: spam or not spam, tumor or benign, fraud or legitimate, the direct application of linear regression fails here—it can produce outputs less than 0 or greater t