Skip to content
AI-grafen
Sign in
☰ Menu

The goal F AI engineering

Fine-tune a model with LoRA

Understand why low-rank adapters work, choose target modules and rank, build a dataset, and evaluate the result with evals.

Knowledge nodes
36
From zero
about 25 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: LoRA on a small network — train only the adaptersFa sandbox · about 75 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
CBuilder8 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
DAI developer16 knowledge nodes
  1. Derivatives and optimisation
  2. Loss functions
  3. Tokenisation
  4. Gradient descent
  5. Vectors
  6. Matrices and matrix multiplication
  7. Linear regression with several features
  8. Neural networks — the forward pass with matrices
  9. Backpropagation
  10. Embeddings — words as vectors
  11. Attention
  12. Overfitting and generalisation
  13. PyTorch — tensors and autograd
  14. Training, validation and test
  15. Train a neural network in PyTorch
  16. Transformers — the architecture
EUniversity4 knowledge nodes
  1. Matrix factorisation and low-rank approximation
  2. Language models — training and generation
  3. Model evaluation
  4. Fine-tuning language models
FAI engineering1 knowledge nodes
  1. LoRA — Low-Rank Adaptation