Update README.md

README.md

@@ -1,3 +1,43 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+tags:
+- neuralnetworks
+- pytorch
+- normaldistribution
+- math
+- noisydata
+---
+# Noisy Gaussian NN – Robustness to Label Noise
+
+## Overview
+This project explores how a simple 1-hidden-layer neural network handles increasing label noise when fitting a Gaussian curve.  
+We test three noise levels (σ = 0.05, 0.1, 0.2) to see when the network smooths effectively and when it starts to underfit.
+
+## Dataset
+- Synthetic dataset: Gaussian curve (`y = exp(-x^2)`)
+- Noise added directly to labels using `torch.normal`
+- 200 evenly spaced `x` points in [-2, 2]
+
+## Model
+- **Architecture:** 1 hidden layer, 50 neurons, `ReLU` activation
+- **Loss:** MSELoss
+- **Optimizer:** Adam (lr=0.01)
+- **Training:** 2000 epochs
+
+## Results
+- Low noise: NN fits curve smoothly.
+- Medium noise: Slight underfitting.
+- High noise: Curve shape lost, noise dominates.
+
+### Key Insight
+> More noise ≠ better regularization.  
+> Too much noise can destroy the signal beyond recovery.
+
+## Files
+- `notebook.ipynb` – Full experiment, plots, and analysis
+- `README.md` – This file
+
+## License
+MIT License – free to use, modify, and distribute with attribution.