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Transfer learning is a technique in machine learning where a model trained on one task is repurposed for another related task. In transfer learning, the knowledge learned by a model on one task can be transferred and used to improve the performance of the model on a related task.

Transfer learning can be particularly useful when the amount of data available for the target task is limited, or when it is expensive or time-consuming to collect new data. By reusing the knowledge learned on a related task, transfer learning can reduce the amount of data and computation required to train a model for the target task.

There are several ways to perform transfer learning, including:

1. Fine-tuning: In fine-tuning, a pre-trained model is retrained on a new task by continuing the training process with new data and adjusting the weights of the network to better fit the new data.

2. Feature extraction: In feature extraction, the pre-trained model is used as a fixed feature extractor, and the extracted features are fed into a new model that is trained on the target task.

3. Model adaptation: In model adaptation, a pre-trained model is modified to better fit the target task by adding or removing layers, adjusting the architecture, or changing the hyperparameters.

Transfer learning has been used successfully in many applications, such as image and speech recognition, natural language processing, and recommendation systems, among others. Transfer learning can help to reduce the amount of data and computation required to train a model, and can lead to faster and more accurate models for new tasks.