Neural net models learn from examples
Training a neural net to learn begins with the process of feeding it millions of labeled examples. In this scenario, the neural net will be shown photos of cats and dogs.
After the model is trained you can test it
After being trained with numerous labeled examples, we can send an unlabeled image through the neural network, and ask the machine to tell us what is in the image.
What does common sense look like to a machine?
In the field they call this “regularization”. The machine should still be able to understand that a dog with a cowboy hat is still a dog. If the example changes slightly, the machine should not totally change its mind. This is done by making the machine learning insensitive to small, unimportant changes.
If at first you don’t succeed, try another billion times
If there is a bumper sticker slogan for machine learning at Google, this is it.