Initial commit: MLP regularization, CNN BatchNorm comparison, autoencoder denoising

autoencoder-denoising/README.md

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+# Autoencoder Image Denoising — MNIST
+
+A simple fully-connected autoencoder trained to reconstruct clean MNIST digits from noisy versions of them.
+
+## Architecture
+
+```
+Input(784) → Dense(32, relu)  [encoder/bottleneck]
+           → Dense(784, sigmoid)  [decoder]
+```
+
+Gaussian noise is added to the input images; the network is trained to output the *clean* version, using binary crossentropy loss between the reconstruction and the original image.
+
+## Approach
+
+1. Load MNIST, normalize pixel values to [0, 1]
+2. Add Gaussian noise (factor 0.5) to create noisy training inputs
+3. Train the autoencoder to map noisy → clean, compressing through a 32-unit bottleneck
+4. Evaluate by visually comparing original, noisy, and reconstructed images
+
+## Result
+
+![Denoising result](autoencoder-denoising-result.png)
+
+Despite the bottleneck compressing each 784-pixel image down to just 32 values, the network recovers a recognizable digit — showing the encoder is learning a meaningful compressed representation rather than just copying pixels through.
+
+## Tech stack
+
+TensorFlow / Keras, NumPy, matplotlib
+
+## Notebook
+
+[`encoder-noisy-image.ipynb`](encoder-noisy-image.ipynb)