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A neural network (also called an “artificial neural network”) is a type of machine learning model that is usually used in supervised learning. By linking together many different nodes, each one responsible for a simple computation, neural networks attempt to form a rough parallel to the way that neurons function in the human brain.
The idea behind neural networks was first born in the 1950s with the perceptron algorithm. A perceptron is a simplified model of a human neuron that accepts an input and performs a computation on that input. The output is then fed to an activation function, which decides whether the neuron will “fire” based on the output value.
While one perceptron cannot recognize complicated patterns on its own, there are thousands, millions, or even billions of connections between the neurons in a neural network. This allows the network to handle even highly complex inputs.
Researchers “train” a neural network over time by analyzing its outputs on different problems and comparing them with the correct answers. Using an algorithm known as backpropagation, the neural network can adjust the influence of any particular node in the network, attempting to reduce the errors that the network makes when calculating a final result.
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https://www.verypossible.com/insights/machine-learning-vs.-neural-networks
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