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The goal E University

Statistics for experiments

Conditional probability, simulation, A/B testing and variance between runs — so that you can tell a real improvement from chance.

Knowledge nodes
69
From zero
about 44 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: 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 minLab: semantic search with embeddingsDa sandbox · about 45 min

The whole path

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

AExplorer5 knowledge nodes
  1. Patterns and categories
  2. Collect data with a survey
  3. Probability and risk in everyday life
  4. Sequences and precise instructions
  5. Integer arithmetic and order of operations
BInvestigator13 knowledge nodes
  1. Data in everyday life
  2. Charts and tables
  3. Algorithmic thinking
  4. Rule-based systems and machine learning
  5. Randomness and probability: basic calculations
  6. Is the chance equal for everyone?
  7. Fractions, decimals, and percentages
  8. Negative numbers in everyday life and AI
  9. Training data, features and labels
  10. Classification: how a model sorts information
  11. Generative AI: how it creates content
  12. Language models and probabilities
  13. AI's invented answers
CBuilder17 knowledge nodes
  1. Functions and coordinate systems
  2. Programming logic — variables, conditions, loops
  3. Python — the basics
  4. Python — lists, loops and dictionaries
  5. Relative frequency and the law of large numbers
  6. Statistics — mean, median and spread
  7. Probability — the basics
  8. Combinatorics
  9. Probability in several steps
  10. Linear regression: fitting a straight line to data
  11. Neural networks — the intuition
  12. Asking effective questions to AI
  13. Compare two AI answers
  14. Evaluate AI performance with your own questions
  15. Variables and algebraic expressions
  16. Powers and roots
  17. Misleading charts
DAI developer23 knowledge nodes
  1. Derivatives and optimisation
  2. Conditional probability
  3. Bayes' theorem
  4. Probability distributions
  5. Loss functions
  6. The normal distribution and standardisation
  7. Samples and uncertainty
  8. Tokenisation
  9. Gradient descent
  10. Better or just different?
  11. Logarithms
  12. Vectors
  13. Matrices and matrix multiplication
  14. Linear regression with several features
  15. Neural networks — the forward pass with matrices
  16. Backpropagation
  17. Embeddings — words as vectors
  18. Attention
  19. Overfitting and generalisation
  20. PyTorch — tensors and autograd
  21. Training, validation and test
  22. Train a neural network in PyTorch
  23. Transformers — the architecture
EUniversity11 knowledge nodes
  1. Monte Carlo methods
  2. Confidence intervals
  3. The bootstrap and resampling
  4. Hypothesis testing and p-values
  5. A/B tests and experiment design
  6. Maximum likelihood
  7. Language models — training and generation
  8. Model evaluation
  9. Scientific method in AI
  10. Reproducibility
  11. Random seeds and the variance between runs