Learn / How LLMs Actually Work
Neural-net foundations
History, a trainable neuron, an N-gram model, and the RNN the transformer replaced. A class in How LLMs Actually Work. Free to start, no card.
Curated lessons in this class
The road to the Transformer
How we got from a single neuron to ChatGPT, in seven steps.
- Sixty years in one map
A neuron from scratch
Build and train a single artificial neuron with NumPy.
- The weighted sum
- Fire or not: the activation
- Wrap it in a predict()
- Learn from one mistake
- Train the AND gate
Predict the next word: an N-gram model
The simplest language model: count what usually comes next.
- Split into words
- Count what follows each word
- Counts into probabilities
- Predict the next word
An RNN cell, unrolled
Carry a hidden state across a sequence, the pre-Transformer way.
- One time step
- Carry the state across a sequence
- Read out a prediction
How CodeTrain teaches this
No videos and no multiple-choice quiz at the end. Each lesson is a handful of small steps in a real editor. You type the code yourself, it runs, and the tutor reviews what you actually wrote. It will not hand you the answer, and the step doesn't advance until your code passes. What you get at the end is a record of which steps you wrote unaided, not a completion percentage.
These curated courses are the starting point, not the product
Everything in this catalog is a curated course we wrote by hand. They exist so there is something to start on. What CodeTrain actually does is build a lesson on demand: name any topic you want to understand and it writes one for you on the spot, or paste any public repository and it teaches you that codebase from the code that is really in it. You are not limited to the list below, and most people stop using it within a session or two.