April 2019
Intermediate to advanced
426 pages
11h 13m
English
An artificial neuron receives one or more input and are multiplied by values known as weights, summed up and passed to an activation function. The final values computed by the activation function makes up the neuron's output. A bias value may be included in the summation term to help fit the data. The following diagram illustrates an artificial neuron:

The summation term can be written as a linear equation such that Z=x1w1+x2w2+...+b. The neuron uses a nonlinear activation function f to transform the input to become the output , and can be written as .
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