Data Science and Machine Learning (Theory and Projects) A to Z - Transfer Learning: What is Transfer learning

The video tutorial explains transfer learning, a technique where a pre-trained model is used as a feature extractor for new tasks. This approach is particularly useful in computer vision, allowing users to leverage models trained on large datasets without needing extensive data or computational resources. By keeping the initial layers fixed and only training the final layers, users can achieve effective results even with limited data. The tutorial outlines the process of implementing transfer learning and highlights its practical benefits.

What is the significance of keeping certain layers fixed during transfer learning?

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What role do the weights of the fully connected layers play in transfer learning?

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Explain how transfer learning can lead to comparable performance with less training data.

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