This course teaches the mechanics behind that claim. Start with a visible neural network, move into GPT-2, then use cost, evals, context, and prompt optimization to decide when training is worth it.
A content designer, PM, support lead, salesperson, or workflow owner often knows more about the task than the person who can train the model. University makes that expertise legible: define the task, inspect outputs, write rubrics, shape context, and only then escalate to training.
The course now starts with Neural Network Playground before GPT-2. First, learners see features, weights, training, overfitting, and model size. Then Transformer Explainer shows how language becomes tokens, embeddings, attention, logits, and sampled text.
Understand how model behavior can improve before anyone changes weights.
Separate hand-written rules from functions learned from examples.
Build an intuition for weights, neurons, layers, parameters, and training.
Connect model size, data, training time, hardware, latency, and unit economics.
Understand why tokens are vocabulary units, not meaning by themselves.
Trace one prompt through embeddings, attention, logits, and next-token prediction.
Use context and evaluation before paying for model training.
Know when to move from prompt optimization to SFT, adapters, rewards, and routing.
Capture repeated LLM work, evaluate it, optimize it, and only then climb the training ladder.
Train a small neural network in the browser while changing datasets, features, layers, activations, and regularization.
Trace tokens through embeddings, attention, transformer blocks, logits, and sampling.
Type a support prompt, inspect token chunks, move temperature, and see why context changes the distribution before sampling does.
Build intuition for orders of magnitude, model size, sequence length, and why scaling wins get expensive quickly.
Click a hand-built embedding space, compare nearest neighbors, and see why relationships can behave like directions.
Watch a repeated prefix become cached keys and values, then compare fresh context with cached continuation.
Start with one weak notification prompt, score variants against a rubric, and inspect the winning prompt.
The demos are self-hosted copies of TensorFlow Playground and Transformer Explainer. Upstream license notices and local hosting notes are preserved in this app.
Model behavior is only half the product decision. This calculator makes the unit economics visible: input tokens, output tokens, cached tokens, and call volume all compound into product margin.
A model vocabulary piece. It might be a word, part of a word, a space, punctuation, or another learned fragment.
A learned numeric representation where token relationships can start carrying useful meaning.
A mechanism for deciding which earlier tokens matter when updating the current token representation.
A learned knob in the model. Larger networks have more knobs and more possible strategies.
A sampling control that changes how sharply the model favors high-probability next tokens. It is not a fix for unclear context.
Changing the information and structure given to the model so the desired answer becomes more likely.
Improving behavior after pretraining through prompts, examples, SFT, adapters, preferences, rewards, or routing.
A saved representation of previous token computation that lets a model continue a generation without rereading the whole prefix.
The practical endpoint is an Understudy run that works on a fresh machine: install prerequisites, choose a provider key, generate prompt candidates, score them with a rubric, inspect failures, and export the winning prompt. The browser walkthrough is live now; the local CLI lab should become the capstone.
This is intentionally soft-launched. The most useful feedback is where the mental model breaks: which demo feels too abstract, which concept needs a bridge, and which exercise would make the lesson stick.
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