Generative Learning Algorithms: GDA, Naive Bayes, and Smoothing
Chapter 4 models p(x|y) and p(y), using GDA, Naive Bayes, and Laplace smoothing to expose both the power and price of generative classification.
#generative-models
2 postsDiffusion Models: Forward Noise, Reverse Generation, and the ELBO
Chapter 14 starts with a fixed Gaussian noising Markov chain and learns to reverse each transition. The ELBO turns reverse-kernel matching into weighted noise prediction, while the continuous-time view explains reverse drift through the score ∇log p_t.