Multi-Layer Perceptron (MLP)

In the previous lessons, we understood how individual neurons work and how gradients help a network learn.

Now we move to the first real deep learning model — the Multi-Layer Perceptron, commonly called MLP.

Most modern deep learning architectures are built on top of this fundamental idea.

Why Single Neurons Are Not Enough

A single neuron (or perceptron) can only learn very simple relationships.

For example, it can separate data using a straight line, but it completely fails when relationships become complex or non-linear.

Real-world problems are rarely linear. This is why we stack neurons into multiple layers.

What Is a Multi-Layer Perceptron?

A Multi-Layer Perceptron is a neural network that consists of:

An input layer, one or more hidden layers, and an output layer.

Each layer transforms the data slightly, allowing the network to learn complex patterns step by step.

How Information Flows in an MLP

When data enters the network:

1. Inputs are multiplied by weights
2. Bias is added
3. Activation functions introduce non-linearity
4. Output moves to the next layer

This process is called forward propagation.

Simple Mathematical View

Each layer performs this operation:

output = activation(weights × input + bias)

Stacking layers means stacking transformations.

This is what gives deep learning its power.

MLP and Real-World Problems

MLPs are widely used for:

Credit risk prediction, customer churn analysis, medical diagnosis, demand forecasting, and many business decision systems.

Any problem where relationships are complex but data is structured fits well with MLPs.

MLP Using Python (Conceptual Example)

Before using large frameworks, let us understand how an MLP looks conceptually in code.

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential([
    Dense(32, activation="relu", input_shape=(10,)),
    Dense(16, activation="relu"),
    Dense(1, activation="sigmoid")
])

model.summary()

At this stage, focus on understanding structure, not memorizing syntax.

What Each Layer Is Doing

The first layer learns basic patterns.

The second hidden layer combines those patterns into higher-level features.

The output layer produces the final prediction.

This layered learning is what makes MLPs powerful.

Where Our Dataset Fits In

Our Dataplexa Deep Learning dataset will soon be used to train MLP models for both regression and classification.

For now, we focus on understanding how MLPs work before feeding real data.

Common Mistakes Beginners Make

Using too many layers without enough data, choosing wrong activation functions, and ignoring overfitting are common issues.

We will address all of these gradually.

Mini Practice

Think carefully:

Why does stacking multiple layers allow the network to learn better representations?

Exercises

Quick Quiz

In the next lesson, we will go deeper into weight initialization strategies and understand why bad initialization can break training even before learning begins.