Fine-tuning is the process of adapting an already trained machine learning model to specific needs or data that may differ from the data on which the model was originally trained. It is a form of transfer learning, where knowledge gained by the model on one task is adapted to a new but related task.
fine-tuning
Fine-tuning is particularly popular in fields where modeling from scratch is costly or impractical due to lack of large amounts of relevant training data.
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Starting point
Rather than starting from scratch, fine tuning uses a model that has already achieved a certain level of performance in solving similar problems. This significantly reduces the time needed for training and may require less data.
Adjustment of parameters
In fine-tuning, existing model weights are slightly modified based on new data. Often, only selected model layers are "thawed" and trained anew, while other layers remain unchanged.
Natural Language Processing (NLP): Adapting a general language model to specific tasks, such as recognizing intentions or classifying emotions.
Image recognition: Adaptation of a model trained on general image databases for specialized applications, such as recognizing specific types of objects.
Recommendations: Adapting recommendation systems to the preferences of specific user groups.
Better performance: the model can better handle the task thanks to specialized customization.
Cost-effectiveness: Requires less computing resources and time than training a model from scratch.
Overfitting: A model can become too specialized and lose its ability to generalize to other, even slightly different data.
Data selection: You need to have the right amount and quality of data that is representative of the new task.
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