Day 9 of self-studying ML — entropy, cross-entropy, and logistic regression notes [D]

Short summary

A self-study ML learner shares notes connecting entropy, KL divergence, and cross-entropy to logistic regression. The key insight is that cross-entropy loss emerges naturally from maximizing the likelihood of labels — taking the log of the likelihood product, negating it yields exactly J(w). Understanding H(p,q) = D(p,q) + H(p) first makes the derivation intuitive rather than memorized.

• •Cross-entropy loss derives directly from maximum likelihood estimation, not just a naming convention
• •Understanding H(p,q) = D(p,q) + H(p) makes logistic regression loss intuitive
• •Full notes available on GitHub; author offers to share their self-study curriculum structure

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