Understanding Multilayer Perceptron (MLP) in Machine Learning

Understanding Multilayer Perceptron (MLP) in Machine Learning

If you’ve just started exploring machine learning, you’ve probably come across the term Multilayer Perceptron (MLP). It’s one of the earliest and most important types of neural networks, and understanding it helps you grasp how modern AI systems work.

What Is a Multilayer Perceptron?

A multilayer perceptron is a type of feedforward neural network. It has:

An input layer that takes features,

One or more hidden layers that process the data, and

An output layer that gives the prediction.

Each neuron in a layer is connected to every neuron in the next layer, forming a fully connected network. The neurons use non-linear activation functions (like ReLU or sigmoid) so that the model can learn complex patterns — not just straight-line relationships.

Why Use MLPs?

A single perceptron can only handle linearly separable data. But many real-world problems — like recognizing handwriting or predicting sales — are non-linear. MLPs solve this by introducing hidden layers that help model these complex patterns.

The training process is powered by backpropagation, an algorithm that adjusts the network’s weights step by step to minimize prediction errors. This is what makes MLPs “learn.”

A Classic Example — XOR Problem

The XOR logic gate is a popular example where single-layer perceptrons fail. MLPs, with their hidden layers, can successfully separate XOR data — proving the strength of non-linear learning.

In short, Multilayer Perceptrons form the base of deep learning. Once you understand them, you’ll have a solid foundation for more advanced models like CNNs and RNNs.