🤖 Neural Network to Solve XOR Non-Linearity Problem ⚡

📌 Requirements

• ☕ Java : The whole code is written in Java ( NO LIBRARY IS NEEDED, YOU CAN COMPILE SIMPLY USING javac ... EXECUTE with java ...)
• 🧠 Basic knowledge of neural networks vs perceptrons
• 🎯 What this project does?

This project implements a simple neural network 🏗️ from scratch in Java to solve the XOR problem ❌🔄⭕. Since XOR is non-linearly separable, a single-layer perceptron cannot solve it ❌. Here, we use a multi-layer neural network 🏆 with backpropagation to make it work! 💡

⚙️ How it works? (It's simple)

• ✅ Feedforward propagation: Computes the output based on given inputs. 🔄
• ✅ Backpropagation: Adjusts weights to reduce errors. 🎯
• ✅ Sigmoid activation function: Helps model non-linearity. 📈
• ✅ Training phase: The network learns by adjusting weights over time. ⏳

📈 Results & Learning Curve

After training, the network should produce accurate results close to 0 and 1! 🎯 You can visualize the learning process using a graph 📊 that shows the error decreasing over time.

🙌 Hopes & Goals

• 💡 I truly hope this first template helps someone out there who is trying to understand how a neural network actually works! 🚀

• 👨‍💻 Feel free to modify, experiment, and improve! 🛠️

• 🎉 Happy coding! 🧑‍💻🔥