Perception, Multilayer Perceptron (MLP) and Neural Network

 1. Perceptron

• What it is: The simplest form of a neural network, introduced by Frank Rosenblatt in 1958.

• Structure: Just one neuron (sometimes extended to a single layer of neurons).

• How it works:

  • Takes several inputs, applies weights, sums them up, and passes the result through an activation function (often a step function in the classic version).

  • Used as a linear classifier (separates data into two classes with a straight line/hyperplane).

• Limitation: Can only solve problems that are linearly separable (e.g., it cannot solve XOR).

---

Multilayer Perceptron (MLP)

• What it is: A feedforward neural network with one or more hidden layers of perceptrons.

• Structure:

  • Input layer → Hidden layer(s) → Output layer.

  • Each perceptron (node) in a layer connects to all perceptrons in the next layer (dense/fully connected).

• Key feature: Uses nonlinear activation functions (ReLU, sigmoid, tanh, etc.), which allow it to solve nonlinear problems like XOR.

• Training: Typically trained using backpropagation + gradient descent.

• Use cases: Classification, regression, pattern recognition, etc.

---

3. Neural Network (General Term)

• What it is: A broader concept — any computational model inspired by the brain’s structure, consisting of layers of interconnected nodes (“neurons”).

• Includes:

  • Simple perceptrons

  • MLPs (feedforward networks)

  • Convolutional Neural Networks (CNNs)

  • Recurrent Neural Networks (RNNs)

  • Transformers, etc.

• So an MLP is a type of neural network, and a perceptron is the simplest building block of them.

---

Hierarchy of concepts:

• Perceptron → single linear classifier neuron.

• Multilayer perceptron (MLP) → a specific type of feedforward neural network with multiple layers of perceptrons.

• Neural network → output is probability, general umbrella term that includes perceptrons, MLPs, CNNs, RNNs, etc.