Transformer Circuits 3: Zero-Layer Transformers
Reading the Framework paper, part 3. What does a transformer with no attention at all learn? Bigram statistics, and that answer turns out to be the key to the direct path in large models.
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Transformer Circuits 2: The Transformer, Redrawn
Reading the Framework paper, part 2. Redrawing the residual stream as a communication channel and attention heads as independent information movers, through virtual weights and the QK/OV split.
Transformer Circuits 1: Summary of Results
Reading the Framework paper, part 1. Treating tiny attention-only models as model organisms for fully reverse engineering transformers, and a map of the key results.
Mechanistic Interpretability: Free Materials to Start
Reverse-engineering neural networks, circuit by circuit. Anthropic’s Transformer Circuits thread and a few companion resources are all free. Where to start, to open this series.
Steering GPT-2's Emotions with Sparse Autoencoders
Finding emotion-related features in GPT-2 using OpenAI’s pretrained SAE, then training one from scratch. Feature patching turns ‘good person’ into ‘shit’.
MacKay's Information Theory: The Free Book and the Lectures
David MacKay’s classic ties information theory, inference, and machine learning into one story, and both the book and the lecture videos are free. Where to get them, to open this series.
K-means, GMM, EM: Three Nested Russian Dolls of Clustering
K-means is actually an extreme case of GMM, and GMM is the canonical application of the EM algorithm. How these three connect within a single framework, and how information geometry explains the relationship.
Reinforcement Learning Lecture 10: RL in Games
David Silver’s Lecture 10. A case study. Best response and Nash equilibrium seen through game theory, minimax search with a binary-linear value function, self-play reinforcement learning, combining minimax with RL, and imperfect-information games like poker. One recipe running through Chinook, Deep Blue, Logistello, TD-Gammon, and Maven.
Reinforcement Learning Lecture 9: Exploration and Exploitation
David Silver’s Lecture 9. Use what you know now (exploitation), or find out more (exploration)? This note ties that dilemma into five principles, then follows the multi-armed bandit through regret and lower bounds, UCB, Thompson sampling, and information-state search, and on to contextual bandits and the extension to MDPs.
Reinforcement Learning Lecture 8: Integrating Learning and Planning
David Silver’s Lecture 8. Learn a model of the environment from experience, then plan with that model. Model learning as supervised learning, the AB example, Dyna and Dyna-Q+, Monte-Carlo Tree Search and Go, TD search and Dyna-2.