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 one of the main advantages of transfer learning in computer vision?

• It allows the use of pre-trained models on new data.

• It requires no data for training.

• It eliminates the need for GPUs.

• It is only applicable to text data.

What information is typically available about a pre-trained model?

• Only the final output.

• The architecture and weight matrices.

• The training data used.

• The exact training time.

How can a pre-trained model be adapted for new tasks?

• By using it as a feature extractor and adding new layers.

• By using it without any modifications.

• By retraining all layers from scratch.

• By changing its architecture completely.

What is the role of the initial convolutional layers in transfer learning?

• They are replaced with new layers.

• They are discarded.

• They are used as feature extractors.

• They are retrained with new data.

Why is transfer learning effective even with small datasets?

• Because it ignores the original dataset.

• Because it only uses the last layer of the model.

• Because it requires no training at all.

• Because it uses pre-trained weights that capture general features.

What is the main benefit of using pre-trained weights in transfer learning?

• They reduce the need for large amounts of new data.

• They increase the complexity of the model.

• They make the model less flexible.

• They require more computational resources.

In transfer learning, what is typically done with the weights of the initial layers?

• They are randomized.

• They are kept fixed and not trainable.

• They are removed from the model.

• They are updated with new data.