Using neural networks in data science
An Artificial Neural Network (ANN), which we will call a neural network, is based on the neuron found in the brain. A neuron is a cell that has dendrites connecting it to input sources and other neurons. Depending on the input source, a weight allocated to a source, the neuron is activated, and then fires a signal down a dendrite to another neuron. A collection of neurons can be trained to respond to a set of input signals.
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An artificial neuron is a node that has one or more inputs and a single output. Each input has a weight assigned to it that can change over time. A neural network can learn by feeding an input into a network, invoking an activation function, and comparing the results. This function combines the inputs and creates an output. If outputs of multiple neurons match the expected result, then the network has been trained correctly. If they don't match, then the network is modified.
A neural network can be visualized as shown in the following...
