Training involves changing the weights of a model's architecture, which are initially random, so that they encode information and produce sensible outputs. Various training approaches, such as supervised and unsupervised training, involve evaluating the model's output and determining how to improve it. The iterative process of training is slow and may not show immediate results. It is a compute-intensive process that takes a long time.

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