Deep Learning CNN Convolutional Neural Networks with Python - What Is Transfer learning

The video tutorial introduces transfer learning, a key component in deep learning architectures. It explains the concept of using pre-trained models on large datasets to extract features, which can then be applied to new, similar datasets. The process involves freezing certain layers of the pre-trained model and adding custom layers, known as head architecture, to address specific classification problems. This approach saves time and resources by avoiding training from scratch and is particularly effective when the new data closely resembles the original training data. The tutorial highlights the widespread use and benefits of transfer learning in various projects.

What types of data can be used with transfer learning?

Explain how a custom head architecture is integrated with a pre-trained model.

Why is transfer learning particularly effective when the new data is similar to the training data?