4
Vectors and embeddings
- Token ID
- One-hot vector
- Learned embedding
Distinguish IDs, , and learned .
Look inside language models, then build with Python
Start here once you have finished beginner steps 1–3.
See beginner steps 1–3 firstWhere are you in the 9 steps?
- Beginner Use AI and check its answers
- Intermediate Look inside a language model
- Advanced Build and evaluate with Python
- Level 1 AI, machine learning, and language models
- Level 2 Write and check a prompt
- Level 3 Text, tokens, and IDs
- Level 4 Vectors and embeddings
- Level 5 Training, inference, and retrieval
- Level 6 Instructions, sampling, and verification
- Level 7 Python: run, inspect, change
- Level 8 Numbers, predictions, and activations
- Level 9 Train and evaluate a small model
Intermediate
Look inside a language model
Follow the examples at your own pace-
-
5
Training, inference, and retrieval
- Training
- Weights
- Inference
Explain what changes in and what a uses at time.
-
6
Instructions, sampling, and verification
- Instructions
- Sampling
- Verification
Compare generation settings without confusing consistency with factual accuracy.
Advanced
Build and evaluate with Python
Small steps, real code-
7
Python: run, inspect, change
- Variables
- Conditions
- Functions
Use variables, conditions, and functions in the .
-
8
Numbers, predictions, and activations
- Input numbers
- Activation function
- Prediction
Build small functions and explain what their outputs mean.
-
9
Train and evaluate a small model
- Split the data
- Train
- Evaluate
Separate training from evaluation and explain the limits of a score.
Where each step gets hands-on
| Step | AI Lab | Build Your Own | Code lab |
|---|---|---|---|
| 4. Vectors and embeddings | Yes | No | No |
| 5. Training, inference, and retrieval | No | Yes | No |
| 6. Instructions, sampling, and verification | No | Yes | No |
| 7. Python: run, inspect, change | No | No | Yes |
| 8. Numbers, predictions, and activations | No | No | Yes |
| 9. Train and evaluate a small model | No | No | Yes |