Cisco AI Black Belt Academy Practice Test

Transfer learning is a technique in machine learning that involves the reuse of a pre-trained model on a new but related task. It leverages knowledge gained while solving one problem and applies it to a different, yet similar, problem. This approach is particularly beneficial when you have limited data for the new task, as it allows you to utilize the features and representations learned by the original model, which was trained on a larger dataset.

In practical terms, transfer learning often involves taking a model that has been trained on a large dataset (for example, a neural network for image recognition trained on millions of images) and fine-tuning it on a smaller, task-specific dataset. This can significantly speed up the training process and improve the performance of the model on the new task, because the base layers of the model have already learned valuable patterns and features from the original dataset.

In contrast, other methods mentioned in the choices do not align with the principle of transfer learning. For instance, unsupervised learning does not utilize pre-trained models but rather works with unlabeled data to find patterns on its own. The notion of focusing solely on image classification is too narrow, as transfer learning can be applied to various tasks beyond just images. Moreover, training models from scratch every time