pre-training and fine-tuning

Pre-training and fine-tuning are two stages in a machine learning process. Pre-training involves training a model on a large dataset, usually unsupervised, with the goal of learning general patterns and features about the data. This is typically done using methods such as autoencoders or language models. Fine-tuning, on the other hand, is the subsequent stage where the pre-trained model is further trained on a smaller labeled dataset that is specific to the desired task. The model learns task-specific knowledge by adjusting its parameters using backpropagation and gradient descent. Overall, pre-training provides a foundation of knowledge to the model, while fine-tuning allows the model to adapt and specialize for the specific task at hand. It helps to improve performance and generalize well on the target task with limited labeled data.

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