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🧠 Single-Layer Perceptron

Welcome to this classic machine learning project! This repository contains a Python implementation of a single-layer perceptron, also known as a simple neural network, proposed by Frank Rosenblatt in 1958 and inspired by the neural model of McCulloch and Pitts (1943).

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📘 Description

The perceptron simulates the basic behavior of a brain neuron:

• It receives inputs 🧾 (x)
• Each input is multiplied by a weight 📊 (w)
• An activation function is applied (z = w·x + b)
• If the result exceeds a certain threshold (θ), the neuron is activated 🔥

This algorithm is a binary linear classifier, used for supervised learning.

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⚙️ Features

• 🐍 Implemented in Python
• 🎯 Performs binary classification
• 📈 Step-by-step training with weight updates
• 🧪 Includes visualizations of training results

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📸 Screenshots

🔹 Decision region example 1

🔹 Decision region example 2

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🚀 How to Run

Make sure you have Python 3 and the required dependencies installed:

pip install -r requirements.txt

Then run the main script:

python main.py

📚 References

• Rosenblatt, F. (1958). The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain.

• McCulloch, W. S., & Pitts, W. (1943). A Logical Calculus of the Ideas Immanent in Nervous Activity.

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