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What do you feel like eating for dinner tonight? Do you feel like pizza, pasta, or perhaps sushi? When you are asked such a question, you would usually provide (some sort of) answer straight away. But how do you come to that decision? Is it based on what you ate earlier today, or is it due to chemicals and nutrients your body is deprived of? Our brain can make many complex decisions in a split second, but we do not fully understand how (some of) these decisions are made.

Artificial Neural Networks (ANN) are statistical models that try to mimic how our brain makes decision, or at least how neurons work. To many of us, they feel like a black-box method, where you get some stimuli (input), and then an action (output) is taken, but it’s not entirely clear what happens in between. In this article, we will investigate what goes on behind the scenes of this black-box technique.

Data Science Report Series #4: A Short Introduction to Artificial Neural Networks, by Shintaro Hagiwara and Patrick Boily.

Post Author: Patrick Boily

Patrick is interested in the applications of mathematics and statistcs to evidence-based decision support. He has worked on 25+ such projects since 2008. He has extensive experience in data science, machine learning, A.I. and predictive analytics, data cleaning and data visualization.