Transfer learning allows us to leverage the knowledge and features learned by pre-trained models and customize them to suit our specific tasks. One common technique is fine-tuning, where we train only a subset of the pre-trained model's layers while keeping the rest fixed.

For example, let's say we have a pre-trained model that was trained on a large dataset to recognize different types of animals. If we want to use this model to perform a more specific task, such as identifying cats and dogs, we can freeze the initial layers responsible for learning general features like edges and shapes, and train only the later layers that are responsible for classifying the specific target classes.

Another way to customize a pre-trained model is by adding additional layers on top of it. These added layers can capture task-specific features and help improve the model's performance on new data. By training these additional layers while keeping the initial layers fixed, we can achieve better results by preserving the general knowledge already captured by the pre-trained model.

Lastly, adjusting the hyperparameters of a pre-trained model can also help in achieving better performance for our specific tasks. Hyperparameters such as learning rate, batch size, and regularization techniques can be fine-tuned to optimize the model's performance on the new dataset.

By fine-tuning and customizing pre-trained models, we can save significant computational resources and achieve impressive results even with limited training data. So, let's dive in and explore the world of transfer learning!