Introduction to Deep Learning 2: Parameters and Configuration

In the first session of our Deep Learning series, we described the basis of our approach to Deep Learning: the classical theory of neural networks. In this second we will try to focus on more practical aspects, such as the use of hyperparameters. One of the most fascinating ideas about Deep Learning is that each layer gets a data representation focused on the objective of the problem to be solved. So, the network as a whole generates an idea of each concept, derived from data. Some questions arise: "how are networks different from each other?" and "how can we build one that represents exactly what we want?"