Phase 2: Probability & Statistics · ~120 minutes · Python

Sampling Methods

Sampling is how AI explores the space of possibilities.

What you will learn

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

Introduction

Type: Build
Language: Python
Prerequisites: Bayes' Theorem
Time: ~120 minutes

Objective

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

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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