In this project, we classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. Here, we preprocessed the data, then train a convolutional neural network on all the samples. We normalize the images, one-hot encode the labels, build a convolutional layer, max pool layer, and fully connected layer. At then end, we inspect their predictions on the sample images.