This week is on MDPs, TD and Bellman's Equation with RL environment. Next week would be on Deep Q-Nets. Sorry for the delay:caught up with work. After q-learning, I will get to Issue #02: on optimizers used along with a general deep dive into DL. Giving the Agent a World:MDPs, Bellman & TD Learning Part 1 showed how an agent picks the best slot machine using ε-greedy and UCB. The key gap left open: bandits have no state. Every pull is independent. Real environments are differ

The Monthly Epoch Why I'm Writing This Every morning, my feed is the same. Thirty-second breakdowns of papers I've already read. Tutorials that rename variables from the official docs and call it a guide. LinkedIn posts about "the future of AI" written by people who've never debugged a gradient tape. I'm not complaining about the internet. I'm just saying — if you're trying to understand why a model stops converging, or how attention actually computes similarity, or what happ