In recent years the concept of deep learning has been gaining widespread attention. The media frequently reports on talent acquisitions in this field, such as those by Google and Facebook, and startups which claim to employ deep learning are met with enthusiasm. Gratuitous comparisons with the human brain are frequent. But is this just a trendy buzz word? What exactly is deep learning and how is it relevant to developments in machine intelligence?
For many researchers, deep learning is simply a continuation of the multi-decade advancement in our ability to make use of large scale neural networks. Let’s first take a quick tour of the problems that neural networks and related technologies are trying to solve, and later we will examine the deep learning architectures in greater detail.
Machine learning generally breaks down into two application areas which are closely related: classification and regression.
In the classification task, you are trying to do automatic recognition. You create a training data set for which you have known labels, for example, images of different types of vegetables. Here you have manually assigned the correct class label, such as yam, carrot, potato, etc, to each one. The images are going to be the input to the algorithm and the class labels are going to be the required output.