Phase 2: Probability & Statistics · ~120 minutes · Python
Sampling Methods
Sampling is how AI explores the space of possibilities.
Hiring signal: Understanding of sampling methods internals
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: Phase 1, Lessons 06-07 (Probability, Bayes' Theorem) Time: ~120 minutes
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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