Training Loop
A training loop repeatedly shows examples, measures error and updates trainable parameters.
A training loop repeatedly shows examples, measures error and updates trainable parameters.
A small group of training examples.
A signal for how wrong the prediction was.
Adjust parameters based on the loss.
Understand the working model
You do not need to memorize every optimization detail to understand the product risk: repeated training can improve the target behavior, but it can also overfit, forget useful behavior or amplify bad examples.
Track experiments with data version, model version, hyperparameters and evaluation results. If you cannot reproduce a run, it is hard to know whether a later change is truly better.
Practice
Apply the pattern above to one real task you already do. Keep the first version small enough that you can inspect every input and output.
Do not keep training just because the training loss continues to decrease.
Treat training as an experiment loop: train, evaluate, inspect failures and change one thing at a time.