small change to project 2

2021-10-12 09:40:05 +02:00
parent a0508abdc2
commit b45f04756e
9 changed files with 14 additions and 14 deletions
@@ -195,7 +195,7 @@ The data sets that we propose here are (the default sets)
 <li> Either the Franke function or the terrain data from project 1, or data sets your propose.</li>
 </ol>

-<li> 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 <a href="https://www.kaggle.com/uciml/breast-cancer-wisconsin-data" target="_self">Wisconsin Breat Cancer Data</a> 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 <a href="https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week40.html" target="_self">week 40</a>. A longer explanation with links to the scientific literature can be found at the <a href="https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29" target="_self">Machine Learning repository of the University of California at Irvine</a>. Feel free to consult this site and the pertinent  literature.</li>
+<li> 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 <a href="https://www.kaggle.com/uciml/breast-cancer-wisconsin-data" target="_self">Wisconsin Breat Cancer Data</a> data set of images representing various features of tumors. These are discussed intensively in the lecture notes, see for example the slides from <a href="https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week40.html" target="_self">week 40</a>. A longer explanation with links to the scientific literature can be found at the <a href="https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29" target="_self">Machine Learning repository of the University of California at Irvine</a>. Feel free to consult this site and the pertinent  literature.</li>
 </ul>

 You can find more information about this at the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html" target="_self">Scikit-Learn site</a> or at the <a href="https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)" target="_self">University of California at Irvine</a>.
@@ -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.

 <p>
-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 <a href="https://seaborn.pydata.org/generated/seaborn.heatmap.html" target="_self">Seaborn package</a> useful when plotting the results as function of the learning rate \( \eta \) and the hyper-parameter \( \lambda \) when you use Ridge regression.

 <h3 id="part-b-writing-your-own-neural-network-code" class="anchor">Part b): Writing your own Neural Network code  </h3>

@@ -195,7 +195,7 @@ The data sets that we propose here are (the default sets)
 <li> Either the Franke function or the terrain data from project 1, or data sets your propose.</li>
 </ol>

-<li> 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 <a href="https://www.kaggle.com/uciml/breast-cancer-wisconsin-data" target="_self">Wisconsin Breat Cancer Data</a> 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 <a href="https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week40.html" target="_self">week 40</a>. A longer explanation with links to the scientific literature can be found at the <a href="https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29" target="_self">Machine Learning repository of the University of California at Irvine</a>. Feel free to consult this site and the pertinent  literature.</li>
+<li> 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 <a href="https://www.kaggle.com/uciml/breast-cancer-wisconsin-data" target="_self">Wisconsin Breat Cancer Data</a> data set of images representing various features of tumors. These are discussed intensively in the lecture notes, see for example the slides from <a href="https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week40.html" target="_self">week 40</a>. A longer explanation with links to the scientific literature can be found at the <a href="https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29" target="_self">Machine Learning repository of the University of California at Irvine</a>. Feel free to consult this site and the pertinent  literature.</li>
 </ul>

 You can find more information about this at the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html" target="_self">Scikit-Learn site</a> or at the <a href="https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)" target="_self">University of California at Irvine</a>.
@@ -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.

 <p>
-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 <a href="https://seaborn.pydata.org/generated/seaborn.heatmap.html" target="_self">Seaborn package</a> useful when plotting the results as function of the learning rate \( \eta \) and the hyper-parameter \( \lambda \) when you use Ridge regression.

 <h3 id="part-b-writing-your-own-neural-network-code" class="anchor">Part b): Writing your own Neural Network code  </h3>

@@ -151,7 +151,7 @@ The data sets that we propose here are (the default sets)
 <li> Either the Franke function or the terrain data from project 1, or data sets your propose.</li>
 </ol>

-<li> 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 <a href="https://www.kaggle.com/uciml/breast-cancer-wisconsin-data" target="_blank">Wisconsin Breat Cancer Data</a> 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 <a href="https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week40.html" target="_blank">week 40</a>. A longer explanation with links to the scientific literature can be found at the <a href="https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29" target="_blank">Machine Learning repository of the University of California at Irvine</a>. Feel free to consult this site and the pertinent  literature.</li>
+<li> 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 <a href="https://www.kaggle.com/uciml/breast-cancer-wisconsin-data" target="_blank">Wisconsin Breat Cancer Data</a> data set of images representing various features of tumors. These are discussed intensively in the lecture notes, see for example the slides from <a href="https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week40.html" target="_blank">week 40</a>. A longer explanation with links to the scientific literature can be found at the <a href="https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29" target="_blank">Machine Learning repository of the University of California at Irvine</a>. Feel free to consult this site and the pertinent  literature.</li>
 </ul>

 You can find more information about this at the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html" target="_blank">Scikit-Learn site</a> or at the <a href="https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)" target="_blank">University of California at Irvine</a>.
@@ -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.

 <p>
-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 <a href="https://seaborn.pydata.org/generated/seaborn.heatmap.html" target="_blank">Seaborn package</a> useful when plotting the results as function of the learning rate \( \eta \) and the hyper-parameter \( \lambda \) when you use Ridge regression.

 <h3 id="part-b-writing-your-own-neural-network-code">Part b): Writing your own Neural Network code  </h3>

@@ -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",
@@ -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
@@ -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
@@ -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  ===