Inputs, labels, sequences, or feedback signals are sampled into batches and evaluated by the training objective. Coverage, quality, permissions, and preprocessing shape what the model can learn and which errors it inherits.
Training data is the collection of examples used to adjust a model's parameters.
Inputs, labels, sequences, or feedback signals are sampled into batches and evaluated by the training objective. Coverage, quality, permissions, and preprocessing shape what the model can learn and which errors it inherits.