Domain Neighborhood
Representation Learning
Embeddings and the geometry of meaning: similarity, normalization, contrastive objectives, and why vector spaces become usable interfaces for models.
Recommended Route
Start here, then follow the prerequisites forward.
This sequence is ordered for learning rather than inventory: lower difficulty, fewer prerequisites, and more central concepts come first.
- 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
Use these after the core path.
In Progress