In this project, we classify images from the CIFAR-10 dataset.
The dataset consists of airplanes, dogs, cats, and other objects. We preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded.
We get to apply what we learned and build a convolutional, max pooling, dropout, and fully connected layers.
At the end, we get to see our neural network's predictions on the sample images.