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

docs/Autoencoder-Denoising.md

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+# Autoencoder Image Denoising — MNIST
+
+## Setup
+
+A minimal fully-connected autoencoder: `Input(784) → Dense(32, relu) → Dense(784, sigmoid)`. Gaussian noise (factor 0.5) is added to MNIST digits, and the network is trained to reconstruct the *original clean* image from the noisy input — binary crossentropy loss between reconstruction and ground truth.
+
+## Why a 32-unit bottleneck matters
+
+Compressing a 784-pixel image down to 32 values and then reconstructing it forces the network to learn which features actually matter for representing a digit, rather than memorizing pixels. If the reconstruction still looks like a recognizable digit after that bottleneck, it's evidence the encoder learned a genuinely useful compressed representation.
+
+## Result
+
+![Original, noisy, and denoised digits](images/autoencoder-denoising-result.png)
+
+Despite the aggressive compression (784 → 32 → 784), the reconstructions are clearly recognizable digits — the noise is substantially removed and the core digit shape is preserved.
+
+## Notebook
+
+[`encoder-noisy-image.ipynb`](../autoencoder-denoising/encoder-noisy-image.ipynb)

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)

docs/Home.md

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+# Deep Learning Portfolio — Documentation
+
+Deep dives into each experiment: architecture, training configuration, and results — illustrated with real plots from the notebooks.
+
+## Projects
+
+- [MLP Regularization](MLP-Regularization.md) — Dropout comparison (with an honest note on a confounded variable)
+- [CNN BatchNorm Comparison](CNN-BatchNorm.md) — clean ablation, BatchNorm's effect isolated
+- [Autoencoder Denoising](Autoencoder-Denoising.md) — noisy-to-clean image reconstruction through a compressed bottleneck

docs/MLP-Regularization.md

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+# MLP Regularization — MNIST
+
+## Setup
+
+A baseline multilayer perceptron (`Dense(500) → Dense(100) → Dense(10)`) compared against the same architecture with `Dropout(0.2)` added after each hidden layer.
+
+## ⚠️ Two variables changed, not one
+
+Before looking at the results: the baseline uses SGD at `lr=0.001`, while the dropout run uses SGD at `lr=0.01` — a 10x higher learning rate, in addition to Dropout being added. This means the comparison below shows what happened between two different configurations, not the isolated effect of Dropout. A rigorous ablation would hold the learning rate constant. This is flagged directly rather than presenting a confounded result as clean evidence for Dropout's effect.
+
+## Training curves
+
+**Baseline:**
+
+![Baseline loss curve](images/mlp-baseline-loss.png)
+
+**With Dropout (and higher learning rate):**
+
+![Dropout loss curve](images/mlp-dropout-loss.png)
+
+## Where each model gets it wrong
+
+**Baseline misclassifications:**
+
+![Baseline misclassified digits](images/mlp-baseline-misclassified.png)
+
+**Dropout version misclassifications:**
+
+![Dropout misclassified digits](images/mlp-dropout-misclassified.png)
+
+## Results
+
+| Model | Test loss |
+|---|---|
+| Baseline (no dropout, lr=0.001) | 0.279 |
+| Dropout (0.2, lr=0.01) | 0.099 |
+
+## Notebooks
+
+- [`mnist-mlp-pred.ipynb`](../mnist-mlp-regularization/mnist-mlp-pred.ipynb)
+- [`mnist-mlp-pred-dropout.ipynb`](../mnist-mlp-regularization/mnist-mlp-pred-dropout.ipynb)