Concept library
638 concepts across 13 domains and 52 tracks. Each track is a coherent sequence — read it top to bottom or dip in wherever the gap is.
All domains
01Foundations
02Transformer Internals
03Training & Fine-Tuning
04Reinforcement Learning
05Inference, Systems & Hardware
06Applied LLM Engineering
07Reasoning, Evaluation & Safety
08Multimodal & Applications
09Classical ML & Statistical Learning
10Causal Inference & Experimentation
11Time Series & Forecasting
12Graphs, Recommenders & Structured Data
15Search & Information Retrieval
12
Graphs, Recommenders & Structured Data
Learning over relations, catalogues and columns rather than free text.
1tracks
3concepts
46cards
0.4hreading
Recommender Systems Matrix factorisation, two-tower retrieval, ranking objectives, feedback loops and cold start. 3 concepts · 46 cards
- 01 Matrix Factorisation and Implicit Feedback Learning low-rank user and item vectors from a sparse interaction matrix, and why the shift from ratings to clicks changes the loss, the negatives and the meaning of the output.
- 02 Feedback Loops and Filter Bubbles A recommender trained on data it generated is optimising against its own past choices, which narrows what users see and makes offline evaluation systematically agree with the incumbent.
- 03 Two-Tower Retrieval and Candidate Generation Splitting the model so that item representations can be precomputed and searched with approximate nearest neighbours, which is what makes recommending from a hundred-million-item catalogue possible at all.