Order a full loop: batch, prediction, loss, gradients, update, then repeat.
Proof of success: explain "Backpropagation and the training loop", complete "The Round of Six Roles" and name the limit of the analogy.
A team trains a paper network to recognize three made-up geometric flags. At each mini-batch, they compute the outputs, measure the loss, propagate the gradients backward and modify the parameters. The learning rate controls the size of the change. After going through all the cards, one epoch is done. Repeating without measuring validation would be dangerous: the model could memorize the training set by heart.
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