A Neural Network Is a Stack of Learned Transformations
Explain the input-hidden-output layer structure of a neural network in business language without losing the mechanism.
A neural network is best explained as a stack of learned transformations, not as a black box. A useful workplace explanation starts with structure. A neural network takes an input vector, pushes it through layers of artificial neurons, and returns an output such as a probability, class, or score. Each neuron computes a weighted combination of the previous layer, adds a bias, applies an activation function, and sends the result forward. One layer can catch simple patterns. Multiple layers can compose patterns, so the model can learn relationships that are hard to write as one clean rule. The important mechanism…
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