Posts tagged "machine-learning"
4 posts found
MaxRL: From REINFORCE to Maximum Likelihood
Why dividing by the number of successes instead of the batch size changes what your gradient estimator optimizes — and how this connects REINFORCE, maximum likelihood, and pass@k through one clean mathematical identity.
reinforcement-learningmachine-learningpolicy-gradient
Reinforcement Learning from Scratch
Building RL from the ground up — actions, rewards, policies, expected reward, the policy gradient theorem, and REINFORCE — all derived step by step with concrete examples.
reinforcement-learningmachine-learningpolicy-gradient
Mathematical Prerequisites for Reinforcement Learning
Building the math foundations you need for RL — probability, expected value, derivatives, the log trick, and Monte Carlo estimation — all through one consistent example.
reinforcement-learningmathematicsmachine-learning
Gradient Boosting: A Complete Guide
A deep dive into Gradient Boosting - from intuition and geometry to the math behind pseudo-residuals, stage-wise corrections, and practical implementation considerations.
machine-learninggradient-boostingensemble-methods