How does an ANN learn?
The learning process of a neural network is configured as an iterative process of the optimization of the weights and is therefore of the supervised type. The weights are modified because of the network's performance on a set of examples belonging to the training set, that is, the set where you know the classes that the examples belong to.
The aim is to minimize the loss function, which indicates the degree to which the behavior of the network deviates from the desired behavior. The performance of the network is then verified on a testing set consisting of objects (for example, images in an image classification problem) other than those in the training set.
ANNs and the backpropagation algorithm
A commonly used supervised
Get Deep Learning with TensorFlow - Second Edition now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
