Home/Tracks

Learning tracks

Routes with an end

256 concepts is a corpus, not a course. Each track below is a bounded route to one destination: every concept it depends on, in the order they build. Computed from the prerequisite graph, so they stay correct as the encyclopedia grows.

Building with retrieval

Grounding a model in your own documents, and why that is harder than it looks.

22
22 concepts3 fields
  1. 1Training Data
  2. 2Natural Language Processing
  3. 3Supervised Learning
  4. 4Unsupervised Learning
  5. 5Curse of Dimensionality
  6. 6Tokenization
  7. 7Perceptron
  8. 8Regression
  9. 9Dimensionality Reduction
  10. 10Token
  11. 11Chunking
  12. 12Neural Network
  13. 13Loss Function
  14. 14Embeddings
  15. 15RNN
  16. 16Attention
  17. 17Self-Supervised Learning
  18. 18Self-Attention
  19. 19Positional Encoding
  20. 20Transformer
  21. 21Large Language Model
  22. 22Retrieval-Augmented Generation

AI safety and alignment

Why making these systems do what we intend is unsolved.

27
27 concepts5 fields
  1. 1Training Data
  2. 2Artificial Intelligence
  3. 3Natural Language Processing
  4. 4Supervised Learning
  5. 5Unsupervised Learning
  6. 6Curse of Dimensionality
  7. 7Tokenization
  8. 8AI Safety
  9. 9Perceptron
  10. 10Regression
  11. 11Dimensionality Reduction
  12. 12Token
  13. 13Neural Network
  14. 14Loss Function
  15. 15Embeddings
  16. 16RNN
  17. 17Transfer Learning
  18. 18Attention
  19. 19Self-Supervised Learning
  20. 20Self-Attention
  21. 21Positional Encoding
  22. 22Transformer
  23. 23Large Language Model
  24. 24Fine-tuning
  25. 25Instruction Tuning
  26. 26RLHF
  27. 27AI Alignment

Reasoning models

The longest chain in the corpus: everything behind models that think before answering.

30
30 concepts3 fields
  1. 1Training Data
  2. 2Natural Language Processing
  3. 3Supervised Learning
  4. 4Unsupervised Learning
  5. 5Curse of Dimensionality
  6. 6Tokenization
  7. 7Perceptron
  8. 8Regression
  9. 9Dimensionality Reduction
  10. 10Token
  11. 11Neural Network
  12. 12Loss Function
  13. 13Embeddings
  14. 14RNN
  15. 15Transfer Learning
  16. 16Attention
  17. 17Self-Supervised Learning
  18. 18Self-Attention
  19. 19Positional Encoding
  20. 20Transformer
  21. 21Large Language Model
  22. 22Prompt Engineering
  23. 23Fine-tuning
  24. 24Instruction Tuning
  25. 25In-Context Learning
  26. 26RLHF
  27. 27Chain-of-Thought
  28. 28Reasoning
  29. 29RLVR
  30. 30Reasoning Model

How these are built. Every concept records what must be understood before it. A track is the full set of prerequisites for one destination, ordered so nothing appears before what it depends on. Nothing here is hand-written curriculum: change the graph and the tracks change.

Progress is read from the concepts you have opened, stored in this browser only. Nothing is gated, and you can jump in anywhere. Want a destination that is not listed? Plan what to learn builds a route to any of the 256 concepts.