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The goal D AI developer

Understand how generative AI works

From vectors and neural networks to attention and transformers — you should be able to explain what happens inside a language model as it writes the next word.

Knowledge nodes
35
From zero
about 20 h
Labs
6
See what you already know — no account

The diagnostic removes what you already know, so your path is usually much shorter.

What you can do afterwards

Labs along the way

You write the code. Tests you cannot see decide whether it holds up.

Lab: semantic search with embeddingsDa sandbox · about 45 minLab: a neural network in pure NumPy — forward, backprop, gradient checkDa sandbox · about 75 minLab: dot product, norm and cosine similarityDin the browser · about 40 minLab: gradient descent from scratchDin the browser · about 50 minLab: matrix multiplication and one layer of a neural networkDin the browser · about 45 minLab: scaled dot-product attention with a causal maskDa sandbox · about 60 min

The whole path

Everything the goal builds on, grouped by level and in the order it builds on itself. Show on the map

AExplorer4 knowledge nodes
  1. Patterns and categories
  2. Sequences and precise instructions
  3. Integer arithmetic and order of operations
  4. Comparing and sorting values
BInvestigator8 knowledge nodes
  1. Data in everyday life
  2. Algorithmic thinking
  3. Rule-based systems and machine learning
  4. Coordinates: positions on a grid
  5. Training data, features and labels
  6. Classification: how a model sorts information
  7. Generative AI: how it creates content
  8. Language models and probabilities
CBuilder10 knowledge nodes
  1. Functions and coordinate systems
  2. Programming logic — variables, conditions, loops
  3. Python — the basics
  4. Python — lists, loops and dictionaries
  5. Statistics — mean, median and spread
  6. Probability — the basics
  7. Linear regression: fitting a straight line to data
  8. Neural networks — the intuition
  9. Attention: how words influence each other
  10. Words as points: similar words close together
DAI developer13 knowledge nodes
  1. Derivatives and optimisation
  2. Gradient descent
  3. The transformer — an overview without formulas
  4. How a language model is trained — at upper-secondary level
  5. Vectors
  6. Matrices and matrix multiplication
  7. Neural networks — the forward pass with matrices
  8. Backpropagation
  9. Embeddings — words as vectors
  10. Attention
  11. PyTorch — tensors and autograd
  12. Train a neural network in PyTorch
  13. Transformers — the architecture