This tutorial discusses transfer learning using TensorFlow for image classification. It highlights the importance of minimizing data collection and labeling efforts by leveraging pre-trained models trained on large datasets like ImageNet. The tutorial covers two main approaches to transfer learning: 1) freezing all but the last layer of a pre-trained model (feature extractor approach), which is useful when the new dataset is closely related to the old one, and 2) allowing all layers to remain trainable (fine-tune-all-layers approach), which is better when the new dataset has more data than the old one. The tutorial demonstrates how to use transfer learning with ResNet50, InceptionV4, and NasNet-A-Large models on the Stanford Dogs dataset. It also provides implementation details, including how to initialize weights from a pre-trained model, train the network with new data, and handle batch normalization layers. The key takeaway is that transfer learning can significantly reduce the burden of data collection and labeling by adapting pre-trained models to new tasks.