In processing data, it adapts some of the techniques from supervised learning.
A machine learning system trained specifically on current customers may not be able to predict the needs of new customer groups that are not represented in the training data.
This is used to make relevant add-on recommendations to customers during the checkout process for online retailers.
Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
When a node receives a numerical signal, it then signals other relevant neurons, which operate in parallel.
They know whether the previous patients had heart attacks within a year.