Unraveling the Basics of Neural Networks

Perceptrons were built in the 1950s, and they proved to be a powerful classifier at the time. A few decades later, researchers realized stacking multiple perceptrons could be more powerful. That turned out to be true, and a multi-layer perceptron (MLP) was born.

A single perceptron works like a neuron in a human brain. It takes multiple inputs, and, like a neuron emits an electric pulse, a perceptron emits a binary pulse which is treated as a response.

The “neuron-like” behavior of perceptrons and an MLP being a “network” of perceptrons perhaps led to the term neural networks coming forth in the early days.

Since their creation, neural networks have ...