diff --git a/doc/Projects/2019/Project2/html/Project2-bs.html b/doc/Projects/2019/Project2/html/Project2-bs.html
index 6106647d0..996a9801f 100644
--- a/doc/Projects/2019/Project2/html/Project2-bs.html
+++ b/doc/Projects/2019/Project2/html/Project2-bs.html
@@ -153,7 +153,7 @@ MathJax.Hub.Config({
 <center><b>Department of Physics, University of Oslo, Norway</b></center>
 <br>
 <p>
-<center><h4>Oct 2, 2019</h4></center> <!-- date -->
+<center><h4>Oct 9, 2019</h4></center> <!-- date -->
 <br>
 <p>
 </div> <!-- end jumbotron -->
@@ -265,7 +265,7 @@ Discuss again your choice of cost function.
 
 <p>
 Train your network and compare the results with those from your Logistic  Regression code. 
-You can test your results against a similar code using <b>Scikit-Learn</b> (see the examples in the above lecture notes) or <b>tensorflow/keras</b>.
+You should test your results against a similar code using <b>Scikit-Learn</b> (see the examples in the above lecture notes) or <b>tensorflow/keras</b>.
 
 <p>
 Comment your results and give a critical discussion of the results
@@ -290,10 +290,17 @@ project 1.
 Compare you results from the neural network regression analysis (with a discussion of learning rates and regularization parameters)
 with those you obtained in project 1. Alternatively, if you opt for other data sets, you would need to run your standard ordinary least squares, Ridge and Lasso calculations using your codes from project 1.
 
+<p>
+Again, we strongly recommend that you compare your own neural Network code and results against a similar code using <b>Scikit-Learn</b> (see the examples in the above lecture notes) or <b>tensorflow/keras</b>.
+
 <h3 id="___sec5" class="anchor">Part e) Critical evaluation of the various algorithms </h3>
 
 <p>
-After all these glorious calculations, you should now summarize the various algorithms and come with a critical evaluation of their pros and cons. Which algorithm works best for the regression case and which is best for the classification case. These codes can also be part of your final project 3, but now applied to other data sets.
+After all these glorious calculations, you should now summarize the
+various algorithms and come with a critical evaluation of their pros
+and cons. Which algorithm works best for the regression case and which
+is best for the classification case. These codes can also be part of
+your final project 3, but now applied to other data sets.
 
 <h2 id="___sec6" class="anchor">Background literature </h2>
 
diff --git a/doc/Projects/2019/Project2/html/Project2.html b/doc/Projects/2019/Project2/html/Project2.html
index 3a9330c2b..d83947f4f 100644
--- a/doc/Projects/2019/Project2/html/Project2.html
+++ b/doc/Projects/2019/Project2/html/Project2.html
@@ -112,7 +112,7 @@ MathJax.Hub.Config({
 <center><b>Department of Physics, University of Oslo, Norway</b></center>
 <br>
 <p>
-<center><h4>Oct 2, 2019</h4></center> <!-- date -->
+<center><h4>Oct 9, 2019</h4></center> <!-- date -->
 <br>
 
 <h2 id="___sec0">Classification and Regression, from linear and logistic regression to neural networks  </h2>
@@ -222,7 +222,7 @@ Discuss again your choice of cost function.
 
 <p>
 Train your network and compare the results with those from your Logistic  Regression code. 
-You can test your results against a similar code using <b>Scikit-Learn</b> (see the examples in the above lecture notes) or <b>tensorflow/keras</b>.
+You should test your results against a similar code using <b>Scikit-Learn</b> (see the examples in the above lecture notes) or <b>tensorflow/keras</b>.
 
 <p>
 Comment your results and give a critical discussion of the results
@@ -247,10 +247,17 @@ project 1.
 Compare you results from the neural network regression analysis (with a discussion of learning rates and regularization parameters)
 with those you obtained in project 1. Alternatively, if you opt for other data sets, you would need to run your standard ordinary least squares, Ridge and Lasso calculations using your codes from project 1.
 
+<p>
+Again, we strongly recommend that you compare your own neural Network code and results against a similar code using <b>Scikit-Learn</b> (see the examples in the above lecture notes) or <b>tensorflow/keras</b>.
+
 <h3 id="___sec5">Part e) Critical evaluation of the various algorithms </h3>
 
 <p>
-After all these glorious calculations, you should now summarize the various algorithms and come with a critical evaluation of their pros and cons. Which algorithm works best for the regression case and which is best for the classification case. These codes can also be part of your final project 3, but now applied to other data sets.
+After all these glorious calculations, you should now summarize the
+various algorithms and come with a critical evaluation of their pros
+and cons. Which algorithm works best for the regression case and which
+is best for the classification case. These codes can also be part of
+your final project 3, but now applied to other data sets.
 
 <h2 id="___sec6">Background literature </h2>
 
diff --git a/doc/Projects/2019/Project2/ipynb/ipynb-Project2-src.tar.gz b/doc/Projects/2019/Project2/ipynb/ipynb-Project2-src.tar.gz
index e1b5fa849..837da8f94 100644
Binary files a/doc/Projects/2019/Project2/ipynb/ipynb-Project2-src.tar.gz and b/doc/Projects/2019/Project2/ipynb/ipynb-Project2-src.tar.gz differ
diff --git a/doc/Projects/2019/Project2/pdf/Project2.p.tex b/doc/Projects/2019/Project2/pdf/Project2.p.tex
index 1239de9c6..9e294cc54 100644
--- a/doc/Projects/2019/Project2/pdf/Project2.p.tex
+++ b/doc/Projects/2019/Project2/pdf/Project2.p.tex
@@ -149,7 +149,7 @@ Project 2 on Machine Learning, deadline November 8
 
 % --- begin date ---
 \begin{center}
-Oct 2, 2019
+Oct 9, 2019
 \end{center}
 % --- end date ---
 
@@ -255,7 +255,7 @@ to use the codes in the above lecture slides as starting points.
 Discuss again your choice of cost function.
 
 Train your network and compare the results with those from your Logistic  Regression code. 
-You can test your results against a similar code using \textbf{Scikit-Learn} (see the examples in the above lecture notes) or \textbf{tensorflow/keras}. 
+You should test your results against a similar code using \textbf{Scikit-Learn} (see the examples in the above lecture notes) or \textbf{tensorflow/keras}. 
 
 Comment your results and give a critical discussion of the results
 obtained with the Logistic Regression code and your own Neural Network
@@ -276,8 +276,16 @@ project 1.
 Compare you results from the neural network regression analysis (with a discussion of learning rates and regularization parameters)
 with those you obtained in project 1. Alternatively, if you opt for other data sets, you would need to run your standard ordinary least squares, Ridge and Lasso calculations using your codes from project 1.
 
+Again, we strongly recommend that you compare your own neural Network code and results against a similar code using \textbf{Scikit-Learn} (see the examples in the above lecture notes) or \textbf{tensorflow/keras}. 
+
+
+
 \paragraph{Part e) Critical evaluation of the various algorithms.}
-After all these glorious calculations, you should now summarize the various algorithms and come with a critical evaluation of their pros and cons. Which algorithm works best for the regression case and which is best for the classification case. These codes can also be part of your final project 3, but now applied to other data sets.
+After all these glorious calculations, you should now summarize the
+various algorithms and come with a critical evaluation of their pros
+and cons. Which algorithm works best for the regression case and which
+is best for the classification case. These codes can also be part of
+your final project 3, but now applied to other data sets.
 
 
 
diff --git a/doc/Projects/2019/Project2/pdf/Project2.pdf b/doc/Projects/2019/Project2/pdf/Project2.pdf
index a374d491a..98f2236ff 100644
Binary files a/doc/Projects/2019/Project2/pdf/Project2.pdf and b/doc/Projects/2019/Project2/pdf/Project2.pdf differ
diff --git a/doc/Projects/2019/Project2/pdf/Project2.tex b/doc/Projects/2019/Project2/pdf/Project2.tex
index 1210054f2..827012c40 100644
--- a/doc/Projects/2019/Project2/pdf/Project2.tex
+++ b/doc/Projects/2019/Project2/pdf/Project2.tex
@@ -123,7 +123,7 @@ Project 2 on Machine Learning, deadline November 8
 
 % --- begin date ---
 \begin{center}
-Oct 2, 2019
+Oct 9, 2019
 \end{center}
 % --- end date ---
 
@@ -229,7 +229,7 @@ to use the codes in the above lecture slides as starting points.
 Discuss again your choice of cost function.
 
 Train your network and compare the results with those from your Logistic  Regression code. 
-You can test your results against a similar code using \textbf{Scikit-Learn} (see the examples in the above lecture notes) or \textbf{tensorflow/keras}. 
+You should test your results against a similar code using \textbf{Scikit-Learn} (see the examples in the above lecture notes) or \textbf{tensorflow/keras}. 
 
 Comment your results and give a critical discussion of the results
 obtained with the Logistic Regression code and your own Neural Network
@@ -250,8 +250,16 @@ project 1.
 Compare you results from the neural network regression analysis (with a discussion of learning rates and regularization parameters)
 with those you obtained in project 1. Alternatively, if you opt for other data sets, you would need to run your standard ordinary least squares, Ridge and Lasso calculations using your codes from project 1.
 
+Again, we strongly recommend that you compare your own neural Network code and results against a similar code using \textbf{Scikit-Learn} (see the examples in the above lecture notes) or \textbf{tensorflow/keras}. 
+
+
+
 \paragraph{Part e) Critical evaluation of the various algorithms.}
-After all these glorious calculations, you should now summarize the various algorithms and come with a critical evaluation of their pros and cons. Which algorithm works best for the regression case and which is best for the classification case. These codes can also be part of your final project 3, but now applied to other data sets.
+After all these glorious calculations, you should now summarize the
+various algorithms and come with a critical evaluation of their pros
+and cons. Which algorithm works best for the regression case and which
+is best for the classification case. These codes can also be part of
+your final project 3, but now applied to other data sets.
 
 
 
diff --git a/doc/src/Projects/2019/Project2/Project2.do.txt b/doc/src/Projects/2019/Project2/Project2.do.txt
index f29d1dd00..fc7ac287b 100644
--- a/doc/src/Projects/2019/Project2/Project2.do.txt
+++ b/doc/src/Projects/2019/Project2/Project2.do.txt
@@ -93,7 +93,7 @@ to use the codes in the above lecture slides as starting points.
 Discuss again your choice of cost function.
 
 Train your network and compare the results with those from your Logistic  Regression code. 
-You can test your results against a similar code using _Scikit-Learn_ (see the examples in the above lecture notes) or _tensorflow/keras_. 
+You should test your results against a similar code using _Scikit-Learn_ (see the examples in the above lecture notes) or _tensorflow/keras_. 
 
 Comment your results and give a critical discussion of the results
 obtained with the Logistic Regression code and your own Neural Network
@@ -115,9 +115,17 @@ project 1.
 Compare you results from the neural network regression analysis (with a discussion of learning rates and regularization parameters)
 with those you obtained in project 1. Alternatively, if you opt for other data sets, you would need to run your standard ordinary least squares, Ridge and Lasso calculations using your codes from project 1.
 
+Again, we strongly recommend that you compare your own neural Network code and results against a similar code using _Scikit-Learn_ (see the examples in the above lecture notes) or _tensorflow/keras_. 
+
+
+
 === Part e) Critical evaluation of the various algorithms ===
 
-After all these glorious calculations, you should now summarize the various algorithms and come with a critical evaluation of their pros and cons. Which algorithm works best for the regression case and which is best for the classification case. These codes can also be part of your final project 3, but now applied to other data sets.
+After all these glorious calculations, you should now summarize the
+various algorithms and come with a critical evaluation of their pros
+and cons. Which algorithm works best for the regression case and which
+is best for the classification case. These codes can also be part of
+your final project 3, but now applied to other data sets.