What are neural networks?
An artificial neural network (ANN) is a computational model inspired by the physiology of animal brains. Similar to the way neurons in the brain send signals to each other, an ANN consists of interconnected nodes (neurons) that map a certain input to a certain output or prediction.
A commonly used form of neural network is the multilayer perceptron (MLP), which contains multiple layers of neurons. As a form of artificial intelligence, MLPs are applied to a multitude of problems and fields, such as facial recognition, automatic translators, and self-driving cars. For problems like these, using conventional algorithms or computer programs is practically impossible. For example, it would take millions of lines of code and a sizable amount of effort to describe to a computer what a face looks like or how to adequately translate every word in a language. AI algorithms, meanwhile, get around this by learning from vast amounts of data in a process called training.
To illustrate this, we will use a common sample problem: predicting the species of a penguin (either Gentoo or Adelie) based on their bill length, flipper length, and body mass. For simplicity, this is be a binary classification problem, although more classes may be added if there is data for it.
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