MLP in Machine Learning: Multilayer Perceptron Guide

Before transformers and chatbots, there was a simpler neural network that started it all. It’s still everywhere today, often hiding inside much bigger models. That network is the multilayer perceptron, and understanding the MLP in machine learning is one of the best first steps into deep learning.

Moreover, once you understand how an MLP works, every other neural network becomes easier to learn. That’s because most of them reuse the same core ideas.

In this guide, the cotrax.ai team explains what an MLP is, how it learns, how it compares with a single perceptron, when to use it, and how to build one in Python.

What Is an MLP in Machine Learning?

A multilayer perceptron (MLP) is a feedforward neural network made of an input layer, one or more hidden layers and an output layer, where every neuron connects to every neuron in the next layer. It uses non-linear activation functions, so it can learn complex patterns for classification and regression tasks.

“Feedforward” means data only moves in one direction, from input to output. There are no loops. Because every neuron links to all neurons in the next layer, these are also called fully connected or dense layers.

New to neural networks? Start with our guide on what neural networks are, and then come back here.

How Does a Multilayer Perceptron Work?

An MLP passes data through its layers in a few clear steps.

1. The input layer takes in features

First, each input neuron holds one feature, such as age, income or the number of site visits. For example, a model with 20 features has 20 input neurons.

2. Hidden layers find patterns

Next, each hidden neuron multiplies its inputs by weights, adds a bias, and applies an activation function like ReLU. As a result, the first hidden layer finds simple patterns, and later layers combine them into richer ones.

3. The output layer makes a prediction

Finally, the output layer turns those patterns into an answer. For classification, it often uses softmax to give a probability for each class. For regression, it simply outputs a number.

How an MLP Learns: Backpropagation

At first, the weights are random, so the predictions are poor. Then the MLP improves through a training loop:

1. Forward pass: the network makes a prediction.
2. Loss: a loss function measures how wrong it was.
3. Backpropagation: the error flows backward to show how each weight contributed.
4. Update: gradient descent nudges every weight to reduce the error.

After many rounds, often called epochs, the MLP learns the pattern in the data. However, if you train too long, it may memorize noise. Our guide to overfitting in machine learning shows how to avoid that.

Perceptron vs Multilayer Perceptron

The MLP grew out of the original perceptron. However, the two differ in one big way.

The XOR problem is a famous example. A single perceptron can’t separate XOR data with one straight line. An MLP, on the other hand, solves it easily with just one small hidden layer. That’s why hidden layers matter so much.

When Should You Use an MLP?

MLPs are flexible, yet they’re not always the best choice. Use this as a quick guide:

• Good fit: structured table data with enough examples, such as churn or demand prediction.
• Good fit: simple signal or sensor data with fixed-length inputs.
• Weaker fit: images, where CNNs and other network types usually win.
• Weaker fit: long text, where transformers lead.
• Weaker fit: small table datasets, where tree models like XGBoost often score higher.

Interestingly, MLPs also live inside bigger models. For instance, every transformer block, the design behind large language models, contains a small MLP called a feedforward layer.

The MLP is the “hello world” of neural networks. Master it, and the bigger models start to feel like variations on a theme.

cotrax.ai Editorial Team

Build an MLP in Python

You can train an MLP in just a few lines using scikit-learn’s MLPClassifier:

from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline

mlp = make_pipeline(
    StandardScaler(),
    MLPClassifier(hidden_layer_sizes=(64, 32), max_iter=500, early_stopping=True)
)
mlp.fit(X_train, y_train)
print(mlp.score(X_test, y_test))

This model has two hidden layers with 64 and 32 neurons. Also, notice the scaler: MLPs train much better when features share a similar range. For larger projects, PyTorch gives you more control, as shown in our guide to deep learning with Python.

Tips for better MLP results

• Scale your inputs so no feature dominates.
• Start small, with one or two hidden layers.
• Use early stopping to avoid overfitting.
• Tune the learning rate, since it affects training more than most settings.
• Compare with a baseline, such as logistic regression or a random forest.

Conclusion

The MLP in machine learning is a simple but powerful neural network. It passes data forward through fully connected layers, learns with backpropagation, and handles curved patterns that a single perceptron can’t.

Use it for table data and as a stepping stone to bigger models. In fact, you’ll find MLP layers inside nearly every modern network, including transformers. So learn it well, and the rest of deep learning gets much easier.

For more beginner-friendly AI guides, keep exploring cotrax.ai.

Sources

• scikit-learn: Neural network models (supervised)
• Rumelhart, Hinton & Williams, Learning representations by back-propagating errors (Nature, 1986)

Frequently asked questions