Deep Learning: What it is and Why it matters

In traditional ML systems, a human (usually a subject matter expert) selects features that are determined to be useful in classification and given as inputs to an ML algorithm. Then, the algorithm learns how to use these features to maximize classification accuracy. In a DL system, features are learned through a mathematical process like backpropagation at every layer of the network. On the first layer of the network, the DL system would learn rudimentary features that can be calculated from the raw input signals. On the second layer, the network learns more complex features using combinations of the features learned on the first layer, and so on.