Convolutional Neural Network

Pixel illumination are the features, the input data,  in CNN models.

CNN helps figure out what kernels, filters, and features to detect all the way backwards using a process called back propagation.

Back propagation is Neural Networks' way to update weights as in Gradient Descent. Back propagation starts from the correct answer, update the weights backwards layer by layer until the final classifications become more correct.

Don't have to see the entire shape. Should be able to recognize partial shapes, obstructed views. For example, a half hidden cow is still a cow.

Should be able to recognize different styles. Van Gough's portrait in post-impressionist style is still Van Gough. It does not change the identity or the classification of the image.