Domain Neighborhood
Representation Learning
Embeddings and the geometry of meaning: similarity, normalization, contrastive objectives, and why vector spaces become usable interfaces for models.
Start here. Predict once, then carry the invariant forward.
xf_θzgeometryLearner lensWhat makes this domain feel navigable?
Take this lensStabilize the first mechanism, make one prediction, then move one node forward.
Recommended Route
Start here, then follow the prerequisites forward.
This sequence is ordered for learning rather than inventory. Published notebooks with an unavailable prerequisite—or a same-domain route step that depends on one—are labeled in the full inventory instead of being presented as ready steps.
- 01Representation Learning & Embedding Geometry
How models turn inputs into vectors whose geometry can expose useful factors, contextual meaning, and similarity structure.
18 mincodedemoafter Scaled Dot-Product Attention & Transformer LayersCheck Scaled Dot-Product Attention & Transformer Layers first if the symbols feel slippery.
- 02Sparse Autoencoders: Feature Dictionaries for Mechanistic Interpretability
Sparse autoencoders learn a reusable dictionary of feature directions so dense model activations can be explained by a small set of interpretable latent factors.
16 mincodedemoafter Representation Learning & Embedding Geometry, superposition, probingWhy this follows: Sparse Autoencoders: Feature Dictionaries for Mechanistic Interpretability uses Representation Learning & Embedding Geometry directly.
All Published Notebooks
Browse the territory.
Representation Learning & Embedding Geometry
How models turn inputs into vectors whose geometry can expose useful factors, contextual meaning, and similarity structure.
Sparse Autoencoders: Feature Dictionaries for Mechanistic Interpretability
Sparse autoencoders learn a reusable dictionary of feature directions so dense model activations can be explained by a small set of interpretable latent factors.
Advanced Bridges