wrapping up here' what you should take away from the exercises in the past two sectionsconvnets are the best type of machine-learning models for computer-vision tasks it' possible to train one from scratch even on very small datasetwith decent results on small datasetoverfitting will be the main issue data augmentation is powerful way to fight overfitting when you're working with image data it' easy to reuse an existing convnet on new dataset via feature extraction this is valuable technique for working with small image datasets as complement to feature extractionyou can use fine-tuningwhich adapts to new problem some of the representations previously learned by an existing model this pushes performance bit further now you have solid set of tools for dealing with image-classification problems--in particular with small datasets licensed to