diff --git a/doc/pub/week39/html/week39-reveal.html b/doc/pub/week39/html/week39-reveal.html
index 8e10ec464..42460a08e 100644
--- a/doc/pub/week39/html/week39-reveal.html
+++ b/doc/pub/week39/html/week39-reveal.html
@@ -303,6 +303,9 @@ ypredictRidge = gridsearch.predict(X_test)
 </pre></div>
 <p>
 By default the grid search function includes cross validation with five folds. The <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" target="_blank">Scikit-Learn documentation</a> contains more information on how to set the different parameters.
+
+<p>
+If we take out the random noise, running the above codes results in \( \lambda=0 \) yielding the best fit.
 </section>
 
 
diff --git a/doc/pub/week39/html/week39-solarized.html b/doc/pub/week39/html/week39-solarized.html
index f8a11d320..7dc0d0002 100644
--- a/doc/pub/week39/html/week39-solarized.html
+++ b/doc/pub/week39/html/week39-solarized.html
@@ -388,6 +388,9 @@ ypredictRidge = gridsearch.predict(X_test)
 <p>
 By default the grid search function includes cross validation with five folds. The <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" target="_blank">Scikit-Learn documentation</a> contains more information on how to set the different parameters.
 
+<p>
+If we take out the random noise, running the above codes results in \( \lambda=0 \) yielding the best fit.
+
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
diff --git a/doc/pub/week39/html/week39.html b/doc/pub/week39/html/week39.html
index 42321f373..6c91bc434 100644
--- a/doc/pub/week39/html/week39.html
+++ b/doc/pub/week39/html/week39.html
@@ -393,6 +393,9 @@ ypredictRidge <span style="color: #666666">=</span> gridsearch<span style="color
 <p>
 By default the grid search function includes cross validation with five folds. The <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" target="_blank">Scikit-Learn documentation</a> contains more information on how to set the different parameters.
 
+<p>
+If we take out the random noise, running the above codes results in \( \lambda=0 \) yielding the best fit.
+
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
diff --git a/doc/pub/week39/ipynb/ipynb-week39-src.tar.gz b/doc/pub/week39/ipynb/ipynb-week39-src.tar.gz
index ac5189fac..2d12ed541 100644
Binary files a/doc/pub/week39/ipynb/ipynb-week39-src.tar.gz and b/doc/pub/week39/ipynb/ipynb-week39-src.tar.gz differ
diff --git a/doc/pub/week39/ipynb/week39.ipynb b/doc/pub/week39/ipynb/week39.ipynb
index 65c5ab2bf..0ed7d500a 100644
--- a/doc/pub/week39/ipynb/week39.ipynb
+++ b/doc/pub/week39/ipynb/week39.ipynb
@@ -176,6 +176,9 @@
    "source": [
     "By default the grid search function includes cross validation with five folds. The [Scikit-Learn documentation](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV) contains more information on how to set the different parameters.\n",
     "\n",
+    "If we take out the random noise, running the above codes results in $\\lambda=0$ yielding the best fit. \n",
+    "\n",
+    "\n",
     "## Randomized Grid Search\n",
     "\n",
     "An alternative to the above manual grid set up, is to use a random\n",
diff --git a/doc/src/week39/week39.do.txt b/doc/src/week39/week39.do.txt
index e01b937df..af50f5af2 100644
--- a/doc/src/week39/week39.do.txt
+++ b/doc/src/week39/week39.do.txt
@@ -139,6 +139,9 @@ print(f"R2 score: {R2(y_test,ypredictRidge)}")
 
 By default the grid search function includes cross validation with five folds. The "Scikit-Learn documentation":"https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" contains more information on how to set the different parameters.
 
+If we take out the random noise, running the above codes results in $\lambda=0$ yielding the best fit. 
+
+
 !split
 ===== Randomized Grid Search =====