Building a Simple Neural Network From Scratch — Lessons Learned

The buzz around artificial intelligence often makes it sound magical. However, at its foundation, machine learning models are just mathematical functions. To really understand them, I built a neural network from scratch without using TensorFlow or PyTorch.

Steps I Followed

1. Mathematical refresher — matrix multiplication, activation functions (sigmoid, ReLU).
2. Forward pass — input × weights → activation → output prediction.
3. Loss function — Mean Squared Error (MSE).
4. Backpropagation — derived gradients, updated weights via stochastic gradient descent.

My Results

Dataset: XOR problem
Network: 2 inputs → 1 hidden layer (4 nodes) → 1 output
After ~1000 iterations, predictions matched expected values with decreasing error.

Key Lessons

• Learning rate — too high = divergence, too low = very slow training.
• Weight initialization matters a lot.
• Visualization of loss helped debugging.

Future Exploration

• Try Leaky ReLU and Softmax functions.
• Build deeper multi-layer networks.
• Train on real datasets like MNIST and compare results.

Takeaway: Coding a network from scratch is challenging but rewarding. It builds intuition about how frameworks actually work under the hood.