chore(edu): archive completed beans and add self-play chapter section tickets [edu-coqp]
Archives completed/scrapped beans from previous chapters (markov, vector db, compiler). Adds section beans for the self-play ML chapter (edu-coqp).main
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# edu-3yw9
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title: 'Write §2: Monte Carlo Tree Search — algorithm explained'
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status: todo
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type: task
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created_at: 2026-03-13T20:03:17Z
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updated_at: 2026-03-13T20:03:17Z
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parent: edu-coqp
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---
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Step-by-step walkthrough of MCTS: selection (UCB1), expansion, simulation/rollout, backpropagation. Include a worked example on a small game tree.
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# edu-453h
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title: 'Write §13: The full AlphaGo Zero training loop'
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status: todo
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type: task
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created_at: 2026-03-13T20:03:17Z
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updated_at: 2026-03-13T20:03:17Z
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parent: edu-coqp
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---
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Reading lesson: generate → train → evaluate → promote. Discuss the ELO-based model selection step and why it matters.
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# edu-4v13
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title: 'Write §8: Exercise 2 — play Tic-Tac-Toe with pure MCTS'
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status: todo
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type: task
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created_at: 2026-03-13T20:03:17Z
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updated_at: 2026-03-13T20:03:17Z
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parent: edu-coqp
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Exercise: wire MCTS to the game logic from Exercise 1 and run a match. Show sample output, discuss iteration count vs strength.
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# edu-5go8
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title: 'Write §3: Why self-play? The AlphaGo Zero insight'
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status: todo
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type: task
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created_at: 2026-03-13T20:03:17Z
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updated_at: 2026-03-13T20:03:17Z
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parent: edu-coqp
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Explain the key insight: a capable engine can be its own teacher. Historical context (AlphaGo vs AlphaGo Zero) and why the approach generalises.
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# edu-7lu6
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title: 'Write §12: Exercise 4 — replace rollout with the value network'
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status: todo
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type: task
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created_at: 2026-03-13T20:03:17Z
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updated_at: 2026-03-13T20:03:17Z
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parent: edu-coqp
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---
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Exercise: substitute random rollout in MCTS with a neural-network value estimate; compare strength before and after.
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# edu-brtk
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title: 'Write §14: Exercise 5 — 1000 self-play games; observe improvement'
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status: todo
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type: task
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created_at: 2026-03-13T20:03:17Z
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updated_at: 2026-03-13T20:03:17Z
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parent: edu-coqp
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---
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Capstone exercise: run the full self-play loop for 1000 games; plot win-rate over iterations; discuss what worked and what didn't.
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# edu-e39n
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title: 'Write §5: Representing game state in Rust'
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status: todo
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type: task
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created_at: 2026-03-13T20:03:17Z
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updated_at: 2026-03-13T20:03:17Z
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parent: edu-coqp
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---
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Reading lesson: design of Board, Player, Move types. Discuss representation trade-offs (bitboard vs array). Show the full type definitions.
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# edu-iv0k
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title: 'Write §9: Neural network architecture overview'
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status: todo
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type: task
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created_at: 2026-03-13T20:03:17Z
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updated_at: 2026-03-13T20:03:17Z
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parent: edu-coqp
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---
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Conceptual lesson: shared convolutional trunk, policy head (move probabilities), value head (win probability). Diagrams encouraged. No code yet.
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# edu-k3tq
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title: 'Write §4: Choosing a simple game — Tic-Tac-Toe'
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status: todo
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type: task
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created_at: 2026-03-13T20:03:17Z
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updated_at: 2026-03-13T20:03:17Z
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parent: edu-coqp
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---
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Explain why Tic-Tac-Toe is ideal: small state space, deterministic, zero-sum, easily verifiable. Foreshadow how the same approach scales to Go/Chess.
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# edu-lqky
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title: 'Write §11: Exercise 3 — train the network on MCTS data'
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status: todo
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type: task
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created_at: 2026-03-13T20:03:17Z
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updated_at: 2026-03-13T20:03:17Z
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parent: edu-coqp
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---
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Exercise: generate training examples (state, policy vector, value) from pure MCTS self-play; run one training epoch; log loss.
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# edu-of9y
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title: 'Write §7: Implementing MCTS in Rust'
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status: todo
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type: task
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created_at: 2026-03-13T20:03:17Z
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updated_at: 2026-03-13T20:03:17Z
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parent: edu-coqp
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---
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Walk through selection (UCB1 formula), expansion, simulation (random rollout), backpropagation. Show Rust code for the node structure and the four phases.
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---
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# edu-pvou
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title: 'Write §10: Integrating a neural network crate'
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status: todo
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type: task
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created_at: 2026-03-13T20:03:17Z
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updated_at: 2026-03-13T20:03:17Z
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parent: edu-coqp
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---
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Reading lesson: evaluate tch-rs vs candle for this use case; show how to define and initialise the network; basic forward-pass usage.
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---
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# edu-wobk
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title: 'Write §1: What is reinforcement learning?'
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status: todo
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type: task
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created_at: 2026-03-13T20:03:17Z
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updated_at: 2026-03-13T20:03:17Z
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parent: edu-coqp
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---
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Cover: state, action, reward, policy, value function. Intuitive explanation with a game-playing example. No code.
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---
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# edu-ymux
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title: 'Write §6: Exercise 1 — implement Tic-Tac-Toe game logic'
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status: todo
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type: task
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created_at: 2026-03-13T20:03:17Z
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updated_at: 2026-03-13T20:03:17Z
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parent: edu-coqp
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---
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Hands-on exercise: move generation, win detection, terminal-state check, displaying the board. Include starter code and expected test output.
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