🧠 Java-SimpleNeuralNetwork

A simple neural network implementation in Java built from scratch, designed to demonstrate the core mechanics of neural networks in an easy-to-understand way.
Perfect for learning how forward propagation and weight training works without heavy libraries.

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🚀 What Is This?

This repository contains a basic feed-forward neural network built entirely in Java, with minimal dependencies.
It’s a learning resource for developers and students who want to see, run, and modify a neural network without relying on frameworks like TensorFlow or DL4J.

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🛠 Features

✔ Written entirely in Java
✔ Feed-forward neural network
✔ Weight initialization and training logic
✔ No external ML libraries
✔ Easy to read and expand

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📦 Getting Started

Prerequisites

• Java 8+
• Maven (optional, if using)

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▶️ Running

Using Maven

If the project uses Maven:

mvn compile
mvn exec:java -Dexec.mainClass="com.yourpackage.Main"

Without Maven

javac -d out/ src/*.java
java -cp out/ Main

🧠 How It Works

This neural network follows a simple process:

1. Inputs pass through layers
2. Weights and biases are applied
3. Activation functions transform values
4. Learning updates weights over iterations

📁 Project Structure

Java-SimpleNeuralNetwork/
├── src/                     # Java source code
│   ├── NeuralNetwork.java   # Neural network core
│   ├── Neuron.java          # Neuron & activation helpers
│   └── Main.java            # Example / entry point
├── docs/                    # Optional documentation
├── examples/                # Example inputs / cases
├── .gitignore
├── pom.xml                  # (If using Maven)
└── README.md