The CIFAR-10 data set consists of 60000 32×32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
Recognizing photos from the cifar-10 collection is one of the most common problems in the today’s world of machine learning. I’m going to show you – step by step – how to build multi-layer artificial neural networks that will recognize images from a cifar-10 set with an accuracy of about 80% and visualize it.
If you want to check the whole project in your browser just open it in your browser using PLON – our data science platform.