diff --git a/doc/Projects/2021/Project2/html/._Project2-bs000.html b/doc/Projects/2021/Project2/html/._Project2-bs000.html
index f9c800422..3658ea708 100644
--- a/doc/Projects/2021/Project2/html/._Project2-bs000.html
+++ b/doc/Projects/2021/Project2/html/._Project2-bs000.html
@@ -195,7 +195,7 @@ The data sets that we propose here are (the default sets)
Either the Franke function or the terrain data from project 1, or data sets your propose.
- Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are the so-called Wisconsin Breat Cancer Data data set of images representing various features of tumors. These are discussed intensively in the lecture notes on neural networks, see for example the slides from week 40. A longer explanation with links to the scientific literature can be found at the Machine Learning repository of the University of California at Irvine. Feel free to consult this site and the pertinent literature.
+ Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are the so-called Wisconsin Breat Cancer Data data set of images representing various features of tumors. These are discussed intensively in the lecture notes, see for example the slides from week 40. A longer explanation with links to the scientific literature can be found at the Machine Learning repository of the University of California at Irvine. Feel free to consult this site and the pertinent literature.
You can find more information about this at the Scikit-Learn site or at the University of California at Irvine.
@@ -231,7 +231,7 @@ results. For Ridge regression you need now to study the results as functions of
the learning rate \( \gamma \). Discuss your results.
-You will need your SGD code for the setup of the Neural Network and Logistic Regression codes.
+You will need your SGD code for the setup of the Neural Network and Logistic Regression codes. You will find the Python Seaborn package useful when plotting the results as function of the learning rate \( \eta \) and the hyper-parameter \( \lambda \) when you use Ridge regression.
Part b): Writing your own Neural Network code
diff --git a/doc/Projects/2021/Project2/html/Project2-bs.html b/doc/Projects/2021/Project2/html/Project2-bs.html
index f9c800422..3658ea708 100644
--- a/doc/Projects/2021/Project2/html/Project2-bs.html
+++ b/doc/Projects/2021/Project2/html/Project2-bs.html
@@ -195,7 +195,7 @@ The data sets that we propose here are (the default sets)
Either the Franke function or the terrain data from project 1, or data sets your propose.
- Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are the so-called Wisconsin Breat Cancer Data data set of images representing various features of tumors. These are discussed intensively in the lecture notes on neural networks, see for example the slides from week 40. A longer explanation with links to the scientific literature can be found at the Machine Learning repository of the University of California at Irvine. Feel free to consult this site and the pertinent literature.
+ Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are the so-called Wisconsin Breat Cancer Data data set of images representing various features of tumors. These are discussed intensively in the lecture notes, see for example the slides from week 40. A longer explanation with links to the scientific literature can be found at the Machine Learning repository of the University of California at Irvine. Feel free to consult this site and the pertinent literature.
You can find more information about this at the Scikit-Learn site or at the University of California at Irvine.
@@ -231,7 +231,7 @@ results. For Ridge regression you need now to study the results as functions of
the learning rate \( \gamma \). Discuss your results.
-You will need your SGD code for the setup of the Neural Network and Logistic Regression codes.
+You will need your SGD code for the setup of the Neural Network and Logistic Regression codes. You will find the Python Seaborn package useful when plotting the results as function of the learning rate \( \eta \) and the hyper-parameter \( \lambda \) when you use Ridge regression.
Part b): Writing your own Neural Network code
diff --git a/doc/Projects/2021/Project2/html/Project2.html b/doc/Projects/2021/Project2/html/Project2.html
index 33aa54c60..1ae046634 100644
--- a/doc/Projects/2021/Project2/html/Project2.html
+++ b/doc/Projects/2021/Project2/html/Project2.html
@@ -151,7 +151,7 @@ The data sets that we propose here are (the default sets)
Either the Franke function or the terrain data from project 1, or data sets your propose.
- Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are the so-called Wisconsin Breat Cancer Data data set of images representing various features of tumors. These are discussed intensively in the lecture notes on neural networks, see for example the slides from week 40. A longer explanation with links to the scientific literature can be found at the Machine Learning repository of the University of California at Irvine. Feel free to consult this site and the pertinent literature.
+ Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are the so-called Wisconsin Breat Cancer Data data set of images representing various features of tumors. These are discussed intensively in the lecture notes, see for example the slides from week 40. A longer explanation with links to the scientific literature can be found at the Machine Learning repository of the University of California at Irvine. Feel free to consult this site and the pertinent literature.
You can find more information about this at the Scikit-Learn site or at the University of California at Irvine.
@@ -187,7 +187,7 @@ results. For Ridge regression you need now to study the results as functions of
the learning rate \( \gamma \). Discuss your results.
-You will need your SGD code for the setup of the Neural Network and Logistic Regression codes.
+You will need your SGD code for the setup of the Neural Network and Logistic Regression codes. You will find the Python Seaborn package useful when plotting the results as function of the learning rate \( \eta \) and the hyper-parameter \( \lambda \) when you use Ridge regression.
Part b): Writing your own Neural Network code
diff --git a/doc/Projects/2021/Project2/ipynb/Project2.ipynb b/doc/Projects/2021/Project2/ipynb/Project2.ipynb
index cd41515a6..b6ea3d965 100644
--- a/doc/Projects/2021/Project2/ipynb/Project2.ipynb
+++ b/doc/Projects/2021/Project2/ipynb/Project2.ipynb
@@ -37,7 +37,7 @@
"a. Either the Franke function or the terrain data from project 1, or data sets your propose.\n",
"\n",
"\n",
- "* Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are the so-called [Wisconsin Breat Cancer Data](https://www.kaggle.com/uciml/breast-cancer-wisconsin-data) data set of images representing various features of tumors. These are discussed intensively in the lecture notes on neural networks, see for example the slides from [week 40](https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week40.html). A longer explanation with links to the scientific literature can be found at the [Machine Learning repository of the University of California at Irvine](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29). Feel free to consult this site and the pertinent literature.\n",
+ "* Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are the so-called [Wisconsin Breat Cancer Data](https://www.kaggle.com/uciml/breast-cancer-wisconsin-data) data set of images representing various features of tumors. These are discussed intensively in the lecture notes, see for example the slides from [week 40](https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week40.html). A longer explanation with links to the scientific literature can be found at the [Machine Learning repository of the University of California at Irvine](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29). Feel free to consult this site and the pertinent literature.\n",
"\n",
"You can find more information about this at the [Scikit-Learn site](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html) or at the [University of California at Irvine](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)). \n",
"\n",
@@ -69,7 +69,7 @@
"results. For Ridge regression you need now to study the results as functions of the hyper-parameter $\\lambda$ and \n",
"the learning rate $\\gamma$. Discuss your results.\n",
"\n",
- "You will need your SGD code for the setup of the Neural Network and Logistic Regression codes.\n",
+ "You will need your SGD code for the setup of the Neural Network and Logistic Regression codes. You will find the Python [Seaborn package](https://seaborn.pydata.org/generated/seaborn.heatmap.html) useful when plotting the results as function of the learning rate $\\eta$ and the hyper-parameter $\\lambda$ when you use Ridge regression.\n",
"\n",
"### Part b): Writing your own Neural Network code\n",
"\n",
diff --git a/doc/Projects/2021/Project2/ipynb/ipynb-Project2-src.tar.gz b/doc/Projects/2021/Project2/ipynb/ipynb-Project2-src.tar.gz
index 7daed6b83..91a0fe134 100644
Binary files a/doc/Projects/2021/Project2/ipynb/ipynb-Project2-src.tar.gz and b/doc/Projects/2021/Project2/ipynb/ipynb-Project2-src.tar.gz differ
diff --git a/doc/Projects/2021/Project2/pdf/Project2.p.tex b/doc/Projects/2021/Project2/pdf/Project2.p.tex
index 4668c209d..90db3aaec 100644
--- a/doc/Projects/2021/Project2/pdf/Project2.p.tex
+++ b/doc/Projects/2021/Project2/pdf/Project2.p.tex
@@ -180,7 +180,7 @@ The data sets that we propose here are (the default sets)
\end{enumerate}
\noindent
-\item Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are the so-called \href{{https://www.kaggle.com/uciml/breast-cancer-wisconsin-data}}{Wisconsin Breat Cancer Data} data set of images representing various features of tumors. These are discussed intensively in the lecture notes on neural networks, see for example the slides from \href{{https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week40.html}}{week 40}. A longer explanation with links to the scientific literature can be found at the \href{{https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29}}{Machine Learning repository of the University of California at Irvine}. Feel free to consult this site and the pertinent literature.
+\item Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are the so-called \href{{https://www.kaggle.com/uciml/breast-cancer-wisconsin-data}}{Wisconsin Breat Cancer Data} data set of images representing various features of tumors. These are discussed intensively in the lecture notes, see for example the slides from \href{{https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week40.html}}{week 40}. A longer explanation with links to the scientific literature can be found at the \href{{https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29}}{Machine Learning repository of the University of California at Irvine}. Feel free to consult this site and the pertinent literature.
\end{itemize}
\noindent
@@ -213,7 +213,7 @@ for example \textbf{Scikit-Learn}'s various SGD options. Discuss your
results. For Ridge regression you need now to study the results as functions of the hyper-parameter $\lambda$ and
the learning rate $\gamma$. Discuss your results.
-You will need your SGD code for the setup of the Neural Network and Logistic Regression codes.
+You will need your SGD code for the setup of the Neural Network and Logistic Regression codes. You will find the Python \href{{https://seaborn.pydata.org/generated/seaborn.heatmap.html}}{Seaborn package} useful when plotting the results as function of the learning rate $\eta$ and the hyper-parameter $\lambda$ when you use Ridge regression.
\paragraph{Part b): Writing your own Neural Network code.}
Your aim now, and this is the central part of this project, is to
diff --git a/doc/Projects/2021/Project2/pdf/Project2.pdf b/doc/Projects/2021/Project2/pdf/Project2.pdf
index 0cf55e304..1cbc3c1c5 100644
Binary files a/doc/Projects/2021/Project2/pdf/Project2.pdf and b/doc/Projects/2021/Project2/pdf/Project2.pdf differ
diff --git a/doc/Projects/2021/Project2/pdf/Project2.tex b/doc/Projects/2021/Project2/pdf/Project2.tex
index f9338c32e..13ae9c648 100644
--- a/doc/Projects/2021/Project2/pdf/Project2.tex
+++ b/doc/Projects/2021/Project2/pdf/Project2.tex
@@ -154,7 +154,7 @@ The data sets that we propose here are (the default sets)
\end{enumerate}
\noindent
-\item Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are the so-called \href{{https://www.kaggle.com/uciml/breast-cancer-wisconsin-data}}{Wisconsin Breat Cancer Data} data set of images representing various features of tumors. These are discussed intensively in the lecture notes on neural networks, see for example the slides from \href{{https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week40.html}}{week 40}. A longer explanation with links to the scientific literature can be found at the \href{{https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29}}{Machine Learning repository of the University of California at Irvine}. Feel free to consult this site and the pertinent literature.
+\item Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are the so-called \href{{https://www.kaggle.com/uciml/breast-cancer-wisconsin-data}}{Wisconsin Breat Cancer Data} data set of images representing various features of tumors. These are discussed intensively in the lecture notes, see for example the slides from \href{{https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week40.html}}{week 40}. A longer explanation with links to the scientific literature can be found at the \href{{https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29}}{Machine Learning repository of the University of California at Irvine}. Feel free to consult this site and the pertinent literature.
\end{itemize}
\noindent
@@ -187,7 +187,7 @@ for example \textbf{Scikit-Learn}'s various SGD options. Discuss your
results. For Ridge regression you need now to study the results as functions of the hyper-parameter $\lambda$ and
the learning rate $\gamma$. Discuss your results.
-You will need your SGD code for the setup of the Neural Network and Logistic Regression codes.
+You will need your SGD code for the setup of the Neural Network and Logistic Regression codes. You will find the Python \href{{https://seaborn.pydata.org/generated/seaborn.heatmap.html}}{Seaborn package} useful when plotting the results as function of the learning rate $\eta$ and the hyper-parameter $\lambda$ when you use Ridge regression.
\paragraph{Part b): Writing your own Neural Network code.}
Your aim now, and this is the central part of this project, is to
diff --git a/doc/src/Projects/2021/Project2/Project2.do.txt b/doc/src/Projects/2021/Project2/Project2.do.txt
index 0de20d371..c09edff79 100644
--- a/doc/src/Projects/2021/Project2/Project2.do.txt
+++ b/doc/src/Projects/2021/Project2/Project2.do.txt
@@ -20,7 +20,7 @@ The data sets that we propose here are (the default sets)
* Regression (fitting a continuous function). In this part you will need to bring back your results from project 1 and compare these with what you get from your Neural Network code to be developed here. The data sets could be
o Either the Franke function or the terrain data from project 1, or data sets your propose.
-* Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are the so-called "Wisconsin Breat Cancer Data":"https://www.kaggle.com/uciml/breast-cancer-wisconsin-data" data set of images representing various features of tumors. These are discussed intensively in the lecture notes on neural networks, see for example the slides from "week 40":"https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week40.html". A longer explanation with links to the scientific literature can be found at the "Machine Learning repository of the University of California at Irvine":"https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29". Feel free to consult this site and the pertinent literature.
+* Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are the so-called "Wisconsin Breat Cancer Data":"https://www.kaggle.com/uciml/breast-cancer-wisconsin-data" data set of images representing various features of tumors. These are discussed intensively in the lecture notes, see for example the slides from "week 40":"https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week40.html". A longer explanation with links to the scientific literature can be found at the "Machine Learning repository of the University of California at Irvine":"https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29". Feel free to consult this site and the pertinent literature.
You can find more information about this at the "Scikit-Learn site":"https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html" or at the "University of California at Irvine":"https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)".
@@ -51,7 +51,7 @@ for example _Scikit-Learn_'s various SGD options. Discuss your
results. For Ridge regression you need now to study the results as functions of the hyper-parameter $\lambda$ and
the learning rate $\gamma$. Discuss your results.
-You will need your SGD code for the setup of the Neural Network and Logistic Regression codes.
+You will need your SGD code for the setup of the Neural Network and Logistic Regression codes. You will find the Python "Seaborn package":"https://seaborn.pydata.org/generated/seaborn.heatmap.html" useful when plotting the results as function of the learning rate $\eta$ and the hyper-parameter $\lambda$ when you use Ridge regression.
=== Part b): Writing your own Neural Network code ===