diff --git a/doc/pub/week36/ipynb/week36.ipynb b/doc/pub/week36/ipynb/week36.ipynb
index b18ca7a7a..7868fc227 100644
--- a/doc/pub/week36/ipynb/week36.ipynb
+++ b/doc/pub/week36/ipynb/week36.ipynb
@@ -1644,7 +1644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "id": "8da16a74",
    "metadata": {},
    "outputs": [
@@ -1659,9 +1659,81 @@
       "1.2167390602128887e-27\n"
      ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.200e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.087e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.974e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.861e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.747e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.634e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.521e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.408e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.295e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.182e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.069e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.956e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.842e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.729e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.616e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.503e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.390e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.277e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.164e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.051e+03, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.374e+02, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.242e+02, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.111e+02, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.980e+02, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.848e+02, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.717e+02, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.586e+02, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.455e+02, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.232e+01, tolerance: 1.453e-02 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_27425/3217402174.py:71: RuntimeWarning: invalid value encountered in log10\n",
+      "  plt.plot(np.log10(lambdas), MSETrain, label = 'MSE Ridge train')\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_27425/3217402174.py:72: RuntimeWarning: invalid value encountered in log10\n",
+      "  plt.plot(np.log10(lambdas), MSEPredict, 'r--', label = 'MSE Ridge Test')\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_27425/3217402174.py:73: RuntimeWarning: invalid value encountered in log10\n",
+      "  plt.plot(np.log10(lambdas), MSELassoTrain, label = 'MSE Lasso train')\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_27425/3217402174.py:74: RuntimeWarning: invalid value encountered in log10\n",
+      "  plt.plot(np.log10(lambdas), MSELassoPredict, 'r--', label = 'MSE Lasso Test')\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1721,7 +1793,8 @@
     "MSETrain = np.zeros(nlambdas)\n",
     "MSELassoPredict = np.zeros(nlambdas)\n",
     "MSELassoTrain = np.zeros(nlambdas)\n",
-    "lambdas = np.logspace(-4, 4, nlambdas)\n",
+    "#lambdas = np.logspace(-4, 10, nlambdas)\n",
+    "lambdas = np.linspace(-4, 10, nlambdas)\n",
     "for i in range(nlambdas):\n",
     "    lmb = lambdas[i]\n",
     "    Ridgebeta = np.linalg.inv(X_train.T @ X_train+lmb*I) @ X_train.T @ y_train\n",