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An artificial neural network (ANN) is a digital architecture that mimics human cognitive processes to model complex patterns, develop predictions, and react appropriately to external stimuli. Structured data is required for many types of machine learning, versus neural networks, which are capable of interpreting events in the world around them as data that can be processed.
Whenever you read a report, watch a movie, drive a car, or smell a flower, billions of neurons in your brain process the information through tiny electric signals. Each neuron processes inputs, and the results are output to the next neuron for subsequent processing, ultimately and instantly producing a business insight, a chuckle, a foot on the brake, or a little joy. In machine learning, neural networks allow digital systems to interpret and react to situations in much the same way.
An ANN is like a brain full of digital neurons, and while most ANNs are rudimentary imitations of the real thing, they can still process large volumes of nonlinear data to solve complex problems that might otherwise require human intervention. For example, bank analysts can use an ANN to process loan applications and predict an applicant’s likelihood of default.
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