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

cnn-batchnorm-comparison/README.md

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+# CNN BatchNorm Comparison — MNIST
+
+Comparing a baseline CNN against the same architecture with Batch Normalization added, on MNIST digit classification.
+
+## Architecture
+
+```
+Conv2D(32, 3x3, stride 2) → Conv2D(64, 3x3, stride 2) → Flatten → Dense(10, softmax)
+```
+
+The BatchNorm version inserts `BatchNormalization` + separate `Activation('relu')` after each conv layer, instead of the activation being baked into the conv layer directly.
+
+## Clean ablation
+
+Both models use the **identical optimizer and learning rate** (SGD, lr=0.001) — Batch Normalization is the only variable that changed. This makes the result directly attributable to BatchNorm, unlike the [MLP regularization comparison](../mnist-mlp-regularization/README.md) in this repo where two variables changed at once.
+
+## Results
+
+| Model | Test loss (categorical crossentropy) |
+|---|---|
+| Baseline CNN | 0.289 |
+| CNN + BatchNorm | 0.094 |
+
+BatchNorm alone (same optimizer, same learning rate) cut test loss by roughly two-thirds — consistent with its known effect of stabilizing and accelerating convergence, especially under plain SGD at a conservative learning rate.
+
+## Tech stack
+
+TensorFlow / Keras, NumPy, matplotlib
+
+## Notebooks
+
+- [`conv-pred.ipynb`](conv-pred.ipynb) — baseline
+- [`conv-pred-BN.ipynb`](conv-pred-BN.ipynb) — with BatchNorm