How does Deep Learning really work?
The most common form of Deep Learning applies to what is called a convolutional neural network, this is a special kind of neural network in which each artificial neurone is connected to a small window over the input or previous layer. For example, in a visual task, each neurone in the first convolution layer will only see a small part of the image, maybe only a few pixels. This convolution layer consists of multiple maps, each searching for a different feature, and each neurone in a map searching for that feature in a slightly different location.
This first layer will come (after some training) to identify useful low level features in the image, such as lines, edges, and gradients in different orientations. This convolution layer is then sub-sampled in what is called a pooling layer, before the whole process starts again with another convolution layer this time finding combinations of the features of the previous layer (lines, corners, curves etc).
As with most neural networks, the parameters or weights of the system start out randomly, and the network will perform poorly. During training however you can program the network what the correct classification of an image is, and over many many examples the network parameters / weights are slowly modified to give the correct classification.