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In this video on “What is transfer learning” we will cover topics like what is transfer learning, How transfer learning works, Why one should choose transfer learning, Steps to perform transfer learning, and popular model training using transfer learning. Transfer learning is a very important concept in the field of computer vision and natural language processing.

✅ What is Transfer Learning?
Transfer learning adapts a pre-trained model from one task to improve performance on a related but different task, particularly when labeled data for the latter is limited. It involves either extracting features and adding task-specific layers or fine-tuning the entire pre-trained model. This approach has successfully enhanced machine learning model performance in various domains.

✅ Is transfer learning better?
Transfer learning allows developers to circumvent the need for lots of new data. A model that has already been trained on a task for which labeled training data is plentiful will be able to handle a new but similar task with far less data. There are other benefits of transfer learning through deep learning as well.

✅ Is transfer learning only for deep learning?
Transfer learning can be applied through several different strategies both in the deep and machine learning space. In this article, I will cover deep learning only techniques called Deep Transfer Learning Strategies.

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