AI Engineering/Phase 01/Lesson 16

Sampling Methods

Sampling is how AI explores the space of possibilities.

Build 2 hr Python

Prerequisites: Phase 1, Lessons 06-07 (Probability, Bayes' Theorem)

#Learning Objectives

Implement inverse CDF, rejection, and importance sampling from scratch using only uniform random numbers
Build temperature, top-k, and top-p (nucleus) sampling for language model token generation
Explain the reparameterization trick and why it enables backpropagation through sampling in VAEs
Run Metropolis-Hastings MCMC to sample from an unnormalized target distribution

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.