"Unlocking the Power of Transfer Learning in Machine Learning"
Transfer Learning in Machine LearningTransfer learning is a technique in machine learning where a pre-trained model is used as a starting point for a new task. Instead of training a model from scratch, transfer learning allows us to leverage the knowledge and experience gained from training a model on a large dataset to solve a new problem with a smaller dataset. Transfer learning is needed because training a deep learning model from scratch requires a large amount of data and computational resources. However, in many real-world scenarios, we may not have access to such resources. Transfer learning allows us to overcome this limitation by using pre-trained models that have already learned useful features from large datasets. We can use transfer learning when we have a small dataset and want to train a model for a new task. Transfer learning can also be used when we want to improve the performance of an existing model by fine-tuning it on a new dataset. There are various examples of transfer learning, such as:
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