TensorFlow 2.0 Tutorial 02: Transfer Learning

This tutorial demonstrates how to perform transfer learning using TensorFlow 2.0, focusing on the process of restoring a pre-trained backbone model and adding custom layers. The key steps involve loading and preprocessing a customized dataset, creating a TensorFlow Dataset from it, and then applying data augmentation techniques such as resizing, cropping, and flipping. Additionally, the tutorial shows how to restore a pre-trained backbone model either through the Keras applications module or by loading it from a `.h5` file. The authors also provide an example of adding new layers to the restored backbone and training the resulting model on a customized dataset. Through this tutorial, users can learn how to leverage pre-trained models for their own tasks while adapting to specific data requirements.

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