Modeled in accordance with the human brain, a Neural Network was built to mimic the functionality of a human brain. The human brain is a neural network made up of multiple neurons, similarly, an Artificial Neural Network (ANN) is made up of multiple perceptrons (explained later).
A neural network consists of three important layers:
a) Input Layer: As the name suggests, this layer accepts all the inputs provided by the programmer.
b) Hidden Layer: Between the input and the output layer is a set of layers known as Hidden layers. In this layer, computations are performed which result in the output.
c) Output Layer: The inputs go through a series of transformations via the hidden layer which finally results in the output that is delivered via this layer.
In information technology, a neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. Neural networks — also called artificial neural networks — are a variety of deep learning technologies.
Commercial applications of these technologies generally focus on solving complex signal processing or pattern recognition problems. Examples of significant commercial applications since 2000 include handwriting recognition for check processing, speech-to-text transcription, oil-exploration data analysis, weather prediction and facial recognition.
A neural network usually involves a large number of processors operating in parallel and arranged in tiers. The first tier receives the raw input information, just like optic nerves in human visual processing. Each successive tier receives the output from the tier preceding it, rather than from the raw input — in the same way neurons farther away from the optic nerve receive signals from those closer to it. The last tier produces the output of the system.
Neural networks are notable for being adaptive, which means they modify themselves as they learn from initial training and subsequent runs provide more information about the world. The most basic learning model is centered on weighting the input streams, which is how each node weights the importance of input from each of its predecessors. Inputs that contribute to getting right answers are weighted higher.
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