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The goal E University

Build an AI service that survives production

Frame the problem correctly, take the prototype to a product with tests and evals, build in degraded modes, and defend against prompt injection.

Knowledge nodes
51
From zero
about 36 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: build an eval harnessEa sandbox · about 60 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

AExplorer2 knowledge nodes
  1. Patterns and categories
  2. Sequences and precise instructions
BInvestigator6 knowledge nodes
  1. Data in everyday life
  2. Algorithmic thinking
  3. Rule-based systems and machine learning
  4. Source criticism and responsibility in AI use
  5. Training data, features and labels
  6. Classification: how a model sorts information
CBuilder11 knowledge nodes
  1. Functions and coordinate systems
  2. Programming logic — variables, conditions, loops
  3. Python — the basics
  4. Python — lists, loops and dictionaries
  5. Python — strings and text processing
  6. Python — files, CSV and JSON
  7. Statistics — mean, median and spread
  8. Probability — the basics
  9. Linear regression: fitting a straight line to data
  10. Neural networks — the intuition
  11. Prompting — steering a language model
DAI developer23 knowledge nodes
  1. Derivatives and optimisation
  2. Python — functions, scope and exceptions
  3. Python — classes and objects
  4. APIs and HTTP
  5. Framing a problem as an ML task
  6. Loss functions
  7. Testing with pytest
  8. Tokenisation
  9. Gradient descent
  10. The context window, system prompts and few-shot
  11. Vectors
  12. Matrices and matrix multiplication
  13. Linear regression with several features
  14. Neural networks — the forward pass with matrices
  15. Backpropagation
  16. Embeddings — words as vectors
  17. Attention
  18. Overfitting and generalisation
  19. PyTorch — tensors and autograd
  20. Retrieval — finding the right text
  21. Training, validation and test
  22. Train a neural network in PyTorch
  23. Transformers — the architecture
EUniversity9 knowledge nodes
  1. Asynchronous programming
  2. Building an API with FastAPI
  3. Language models — training and generation
  4. Model evaluation
  5. Build an eval harness
  6. From prototype to product
  7. Fallback and degradation in AI services
  8. RAG — retrieval-augmented generation
  9. Prompt injection