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Transfer learning accelerates model training using pretrained models from "summary" of Machine Learning For Dummies by John Paul Mueller,Luca Massaron
Transfer learning is a powerful concept in machine learning that leverages pretrained models to accelerate the training process of new models. Instead of starting from scratch with training a new model, transfer learning allows you to take advantage of the knowledge learned by a pretrained model on a similar task. By using the knowledge encoded in the pretrained model, you can significantly reduce the time and computational resources needed to train a new model. The idea behind transfer learning is to transfer the knowledge gained by a model trained on a large dataset to a new model that is intended for a related but slightly different task. This transfer of knowledge can help the new model learn faster and achieve better performance than if it were trained from scratch. Instead of learning everything from the beginning, the new model can focus on learning the specific nuances of the new task, building o...
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