🧠 NeuXor — The XOR Neural Network

A minimal neural network built entirely from scratch using NumPy — designed to learn the classic XOR logic function.

Note

This project is experimental and intended for learning purposes only.

📌 Project Goal

To demystify how neural networks work under the hood by building one from scratch —
no libraries like TensorFlow or PyTorch — just pure matrix math using NumPy.

This project solves the XOR problem, which is not linearly separable (i.e., it can't be solved by a straight line).
We use 1 hidden layer with 2 neurons, and sigmoid activations.

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📚 Learning Objectives

• Understand forward and backward propagation
• Learn how weights and biases are updated
• See how activation functions help neural nets learn
• Build confidence to create deeper nets from scratch

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🧠 Neural Network Architecture

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🧪 XOR Dataset

Input (X1, X2)	Output
0, 0	0
0, 1	1
1, 0	1
1, 1	0

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🧾 Code Structure

Section	What It Does
X, Y	Input/Output values for XOR
sigmoid()	Activation function
derivative()	Used in backprop to adjust weights
w1, w2, b1, b2	Random weights and biases for layers
Forward Pass	Calculates output
Backward Pass	Calculates how wrong and how to fix it
Weight Update	Adjusts weights and biases to improve accuracy
Output	Final predictions after training

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🛠 Technologies

• Python 3.10+
• NumPy

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📈 Sample Output

Epoch 0 | Loss: 0.3312

Epoch 1000 | Loss: 0.1247

Epoch 2000 | Loss: 0.0611

...

Final Output: [[0.02] [0.98] [0.97] [0.05]]

📂 Run Locally

git clone (https://github.com/Elitsuv/NeuXor.git)
cd .
python network.py