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What is a multi-layered perceptron?

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A multi-layered perceptron (MLP) is one of the most common neural network models used in the field of deep learning. Often referred to as a “vanilla” neural network, an MLP is simpler than the complex models of today’s era. However, the techniques it introduced have paved the way for further advanced neural networks.

The multilayer perceptron (MLP) is used for a variety of tasks, such as stock analysis, image identification, spam detection, and election voting predictions.

The Basic Structure

A multi-layered perceptron consists of interconnected neurons transferring information to each other, much like the human brain. Each neuron is assigned a value. The network can be divided into three main layers.

Input Layer

This is the initial layer of the network which takes in an input which will be used to produce an output.

Hidden Layer(s)

The network needs to have at least one hidden layer. The hidden layer(s) perform computations and operations on the input data to produce something meaningful.

Output Layer

The neurons in this layer display a meaningful output.