Keras

Cifar-10 is an image classification subset widely used for testing image classification AI. I have seen lots and lots of articles like "Reaching 90% Accuracy for Cifar-10", where they build complex convolutional neural networks, add data augmentation, and reach 90% to 95%. It's interesting, that when these articles usually show how a CNN (networks) acts without data augmentation, they usually end up somewhere around 75%. But you can, in fact, squeeze more from a CNN without data augmentation by adjusting hyper-parameters better. I got myself up to 83.4%.