Training is the process through which an
AI model "
learns" to perform its task by analyzing large amounts of data (
datasets). It’s similar to how a student
learns through practice and examples, but on a much larger scale and at much higher speeds.
During training, the model analyzes thousands or millions of examples to identify
patterns and
learn from them. For instance, to recognize cats in photos, a model would be trained with millions of labeled cat images, gradually
learning to identify ears, whiskers, and other feline features.
The process involves several stages: first,
training data is collected and prepared, then the model's
parameters are adjusted as it processes the data, and finally, its performance is tested with new, unseen data. It’s like a cycle of study, practice, and exams.
The results of training heavily depend on the quality and quantity of the data used. If the data is biased or incorrect, the model will
learn those same
biases, similar to how a student might
learn from error-filled textbooks.