Add docs with detailed write-ups and result visualizations

docs/Linear-Models-Deep-Dive.md

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+# Linear Models Deep Dive
+
+Building linear models from their mathematical foundations — normal equation and gradient descent implemented by hand — before confirming each result against scikit-learn.
+
+## Gradient descent convergence
+
+Implementing batch gradient descent manually and watching the cost function actually decrease iteration by iteration is what makes the "why" behind `.fit()` concrete instead of abstract:
+
+![Gradient descent convergence](images/lin-gd-convergence.png)
+
+## The bias-variance tradeoff, visualized
+
+Learning curves make the abstract concept concrete — comparing training error against validation error as training set size grows:
+
+![Learning curve](images/lin-learning-curve.png)
+
+Small training sizes show both errors high (underfitting / high bias). As more data is added, the gap between train and validation error reveals how much the model was overfitting to noise.
+
+## How regularization actually shrinks coefficients
+
+Ridge, Lasso, and ElasticNet all penalize large coefficients, but differently — this plot shows coefficient magnitude as the penalty strength (α) increases:
+
+![Regularization effect on coefficients](images/lin-regularization.png)
+
+Lasso's tendency to zero out weak features entirely (vs. Ridge's smooth shrinkage) is visible directly in the coefficient paths, not just in the theory.
+
+## Multiclass decision boundaries
+
+Softmax regression on the Iris dataset — each region shows which class the model assigns highest probability to:
+
+![Multiclass decision boundaries](images/lin-decision-boundaries.png)
+
+## Notebooks
+
+- [`linear_models_notebook.ipynb`](../linear-models/linear_models_notebook.ipynb) — original work
+- [`linear_models_notebook_polished.ipynb`](../linear-models/linear_models_notebook_polished.ipynb) — AI-assisted documentation pass