What is Deep Learning?

Simply put, training a deep learning model means that you're feeding data to the model, getting an output, and then using that output to make adjustments. For example, if you train your model on a bunch of pictures of cats and then feed it new cat photos it's never seen before, it should be able to pick out the cats in the new photos. If it doesn't, you can change the way the network's nodes are weighing certain characteristics of the images (the presence of whiskers and a tail, for instance). Weight, in this case, is a number that represents the importance of a characteristic. The higher the weight, the higher the influence that characteristic has on the nodes.