Unraveling the Basics of Neural Networks
Explore the progression of perceptrons to MLPs, their architecture, and deep learning fundamentals.
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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 come a long way. Tremendous advancements have been made in ...