In homage to John Hopfield and Geoffrey Hilton, Nobel Prize winners for their “fundamental discoveries and inventions that made machine learning and

It all started with a Perceptron

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2024-10-27 17:00:27

In homage to John Hopfield and Geoffrey Hilton, Nobel Prize winners for their “fundamental discoveries and inventions that made machine learning and artificial neural networks possible,” I propose to explore the foundations of connectionist AI. We will examine how, by drawing inspiration from the functioning of the human brain, we can design algorithms capable of learning from data.

The Perceptron, invented in 1957 by Frank Rosenblatt, is a supervised learning algorithm. It is a basic machine learning model primarily used for classification tasks. It is one of the simplest algorithms in the field of neural networks and is often considered a foundational building block for more complex models, such as multilayer neural networks.

In summary, the Perceptron applies a simple rule: it multiplies each input feature by a weight, sums them, and if the sum exceeds a threshold, it assigns a positive class; otherwise, a negative class.

This process repeats for all training examples until the model converges, i.e., the weights no longer change, or the error becomes sufficiently small.