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

docs/CNN-BatchNorm.md

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+# CNN BatchNorm Comparison — MNIST
+
+## Setup
+
+A baseline CNN (`Conv2D(32) → Conv2D(64) → Flatten → Dense(10, softmax)`) compared against the identical architecture with BatchNormalization inserted after each conv layer. Both models use the same optimizer and learning rate (SGD, lr=0.001) — BatchNorm is the only variable that changed, making this a clean ablation.
+
+## Training curves
+
+**Baseline CNN:**
+
+![Baseline CNN loss curve](images/cnn-baseline-loss.png)
+
+**CNN + BatchNorm:**
+
+![BatchNorm CNN loss curve](images/cnn-bn-loss.png)
+
+The BatchNorm version's curve drops faster and settles lower — visible directly in the training dynamics, not just the final number.
+
+## Results
+
+| Model | Test loss |
+|---|---|
+| Baseline CNN | 0.289 |
+| CNN + BatchNorm | 0.094 |
+
+Since the optimizer and learning rate were held constant, this ~3x reduction in test loss is attributable to BatchNormalization itself — consistent with its known role in stabilizing gradients and accelerating convergence.
+
+## Notebooks
+
+- [`conv-pred.ipynb`](../cnn-batchnorm-comparison/conv-pred.ipynb)
+- [`conv-pred-BN.ipynb`](../cnn-batchnorm-comparison/conv-pred-BN.ipynb)