Dataset
A dataset is the organised collection of examples an AI learns from or is judged on: thousands or millions of rows of text, images or sensor readings, cleaned, labelled and split into training, validation and test parts.
No jargon, no math
In plain words, what is it?
Everything a model knows comes from its dataset, so the dataset is where quality is won or lost. Rows arrive raw: logs, photos, documents. You clean them (drop duplicates, fix labels) and label them (is this weld OK?). Then you split them three ways: the training set, which the model learns from; the validation set, where you tune the recipe; and the test set, locked away until the end as a fair exam. The rule that decides everything: garbage in, garbage out. A model trained on 5,000 sloppy labels loses to one trained on 2,000 clean ones. It breaks quietly: biased or unbalanced data teaches biased behaviour, and the model will never warn you about that.
Terms that live nearby.
Training
Training is the process that turns an untrained model into a working AI: you feed it thousands or millions of examples, compare its guesses with the correct answers, and nudge its internal settings until the guesses get good.
ReadOverfitting
Overfitting is when a model memorises its training data instead of learning the underlying pattern: near-perfect on examples it has seen, unreliable on anything new.
ReadSynthetic Data
Synthetic data is artificial data engineered to reproduce the statistical patterns of real data: a model or a set of rules generates new records that behave like the originals without containing any real person's or customer's information.
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