Skip to content
AI-grafen
Sign in
☰ Menu

The goal E University

Build an NLP system end to end

University level. From statistical learning and transformers to a served text system with evals, tests and documentation.

Knowledge nodes
47
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
BInvestigator5 knowledge nodes
  1. Data in everyday life
  2. Algorithmic thinking
  3. Rule-based systems and machine learning
  4. Training data, features and labels
  5. Classification: how a model sorts information
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. 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
DAI developer21 knowledge nodes
  1. Derivatives and optimisation
  2. Python — functions, scope and exceptions
  3. Python — classes and objects
  4. APIs and HTTP
  5. Loss functions
  6. Testing with pytest
  7. Tokenisation
  8. Gradient descent
  9. Precision, recall, F1 and ROC
  10. Vectors
  11. Matrices and matrix multiplication
  12. Linear regression with several features
  13. Neural networks — the forward pass with matrices
  14. Backpropagation
  15. Embeddings — words as vectors
  16. Attention
  17. Overfitting and generalisation
  18. PyTorch — tensors and autograd
  19. Training, validation and test
  20. Train a neural network in PyTorch
  21. Transformers — the architecture
EUniversity9 knowledge nodes
  1. Asynchronous programming
  2. Building an API with FastAPI
  3. BERT and masked language modelling
  4. Language models — training and generation
  5. Model evaluation
  6. Build an eval harness
  7. Text classification with transformers
  8. Project E: an NLP system end to end
  9. Scientific method in AI