Neural Network Training with Multilayer Perceptron (MLP)

Photo by Milad Fakurian on Unsplash

A Multilayer Perceptron (MLP) is a type of artificial neural network architecture that consists of multiple layers of nodes (neurons or perceptrons) arranged in a feedforward manner. It is a versatile and widely used model in machine learning, particularly in tasks such as classification, regression, and pattern recognition.

1. Architecture

• Input Layer: The first layer of the network that receives the input features.
• Hidden Layers: Intermediate layers between the input and output layers. Each layer contains multiple neurons, and the number of hidden layers and neurons per layer can vary.
• Output Layer: The final layer that produces the model’s output.

      Input Layer
           ↓
     ----------------
    | Input Neuron 1 |
     ----------------
           ↓
     ----------------
    | Input Neuron 2 |
     ----------------
           ↓
            .
            .
            .
           ↓
     ----------------
    | Input Neuron N |
     ----------------
           ↓
     ________________
    |                |
    | Hidden Layer 1 |
    | (Neurons)      |
    |________________|
           ↓
     ________________
    |                |
    | Hidden Layer 2 |
    | (Neurons)      |
    |________________|
           ↓
            …