What is a Perceptron?

A perceptron is a type of artificial neural network that uses artificial intelligence to track features and input data. It is a machine-based algorithm used for supervised learning of binary sorting tasks. Perceptrons are simple artificial neurons that take binary inputs and produce a binary output. They were introduced by Frank Rosenblatt in 1958 and are also known as McCulloch-Pitts neurons.

How does a Perceptron function as a basic unit of a neural network?

A perceptron functions as a basic unit of a neural network by taking multiple input values, each with an associated weight that represents the importance of that input.

These weighted inputs are summed up, and if the sum exceeds a certain threshold, the perceptron produces an output. This output is determined by applying an activation function to the weighted sum. Perceptrons are typically arranged in layers to form more complex neural networks capable of learning and making decisions based on input data.

What are the limitations of Perceptrons in modeling complex functions?

However, perceptrons have limitations in modeling complex functions due to their linear nature and inability to handle non-linear relationships in data. They can only learn linearly separable patterns, which restricts their ability to solve more complex problems. To overcome this limitation, more advanced neural network architectures, such as multilayer perceptrons and deep learning models, have been developed to handle non-linear data and perform more sophisticated tasks.