Example Concept: Transfer learning involves taking a neural network trained on a large dataset for a specific task, such as image classification, and using its learned features as a starting point for a new task. The initial layers of the network, which capture generic features, are retained, while the final layers are fine-tuned to adapt to the new task. This process significantly reduces the amount of data and computational resources needed for training, as the model already has a solid foundation of knowledge.