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The goal F AI engineering

Generative models in depth

Diffusion, GANs and how you actually evaluate a model that creates content — including checking for memorisation.

Knowledge nodes
63
From zero
about 52 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

AExplorer3 knowledge nodes
  1. Patterns and categories
  2. Sequences and precise instructions
  3. Integer arithmetic and order of operations
BInvestigator8 knowledge nodes
  1. Data in everyday life
  2. Algorithmic thinking
  3. Rule-based systems and machine learning
  4. Binary numbers and bits
  5. Fractions, decimals, and percentages
  6. Negative numbers in everyday life and AI
  7. Training data, features and labels
  8. 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. Statistics — mean, median and spread
  6. Probability — the basics
  7. Digital representation: text, images and audio
  8. Linear regression: fitting a straight line to data
  9. Neural networks — the intuition
  10. Variables and algebraic expressions
  11. Powers and roots
DAI developer19 knowledge nodes
  1. Derivatives and optimisation
  2. Integrals — the basics
  3. Loss functions
  4. Tokenisation
  5. Gradient descent
  6. Logarithms
  7. Vectors
  8. Matrices and matrix multiplication
  9. Linear regression with several features
  10. Neural networks — the forward pass with matrices
  11. Backpropagation
  12. Embeddings — words as vectors
  13. Attention
  14. NumPy — arrays and vectorisation
  15. Overfitting and generalisation
  16. PyTorch — tensors and autograd
  17. Training, validation and test
  18. Train a neural network in PyTorch
  19. Transformers — the architecture
EUniversity14 knowledge nodes
  1. Integrals in probability: the expectation
  2. Information theory: entropy and KL divergence
  3. Linear maps
  4. Eigenvalues and eigenvectors
  5. Matrix factorisation and low-rank approximation
  6. Convolutional networks (CNNs)
  7. Generative models — an overview
  8. Image preprocessing and augmentation
  9. Singular value decomposition (SVD)
  10. PCA — principal component analysis
  11. Autoencoders
  12. Language models — training and generation
  13. Model evaluation
  14. Vision Transformer (ViT)
FAI engineering6 knowledge nodes
  1. Diffusion models
  2. GANs
  3. CLIP and contrastive image–text learning
  4. Latent diffusion and text-to-image
  5. Evaluating generative models
  6. Variational autoencoders
GFrontier Lab2 knowledge nodes
  1. Diffusion models — the mathematics
  2. Flow matching