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typo in p2

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Morten Hjorth-Jensen
2021-10-20 09:50:37 +02:00
parent 97f4edb10b
commit 86358bc28a
9 changed files with 13 additions and 13 deletions
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doc/Projects/2021/Project2/html/._Project2-bs000.html
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@@ -165,7 +165,7 @@ MathJax.Hub.Config({
<center><b>Department of Physics, University of Oslo, Norway</b></center>
<br>
<p>
<center><h4>Oct 12, 2021</h4></center> <!-- date -->
<center><h4>Oct 20, 2021</h4></center> <!-- date -->
<br>
<p>
</div> <!-- end jumbotron -->
@@ -228,7 +228,7 @@ epochs as well as algorithm for scaling the learning rate. You can
also compare your own results with those that can be obtained using
for example <b>Scikit-Learn</b>'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.
the learning rate \( \eta \). Discuss your results.
<p>
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.
doc/Projects/2021/Project2/html/Project2-bs.html
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@@ -165,7 +165,7 @@ MathJax.Hub.Config({
<center><b>Department of Physics, University of Oslo, Norway</b></center>
<br>
<p>
<center><h4>Oct 12, 2021</h4></center> <!-- date -->
<center><h4>Oct 20, 2021</h4></center> <!-- date -->
<br>
<p>
</div> <!-- end jumbotron -->
@@ -228,7 +228,7 @@ epochs as well as algorithm for scaling the learning rate. You can
also compare your own results with those that can be obtained using
for example <b>Scikit-Learn</b>'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.
the learning rate \( \eta \). Discuss your results.
<p>
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.
doc/Projects/2021/Project2/html/Project2.html
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@@ -123,7 +123,7 @@ MathJax.Hub.Config({
<center><b>Department of Physics, University of Oslo, Norway</b></center>
<br>
<p>
<center><h4>Oct 12, 2021</h4></center> <!-- date -->
<center><h4>Oct 20, 2021</h4></center> <!-- date -->
<br>
<h2 id="classification-and-regression-from-linear-and-logistic-regression-to-neural-networks">Classification and Regression, from linear and logistic regression to neural networks </h2>
@@ -184,7 +184,7 @@ epochs as well as algorithm for scaling the learning rate. You can
also compare your own results with those that can be obtained using
for example <b>Scikit-Learn</b>'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.
the learning rate \( \eta \). Discuss your results.
<p>
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.
doc/Projects/2021/Project2/ipynb/Project2.ipynb
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@@ -10,7 +10,7 @@
"<!-- Author: --> \n",
"**[Data Analysis and Machine Learning FYS-STK3155/FYS4155](http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html)**, Department of Physics, University of Oslo, Norway\n",
"\n",
"Date: **Oct 12, 2021**\n",
"Date: **Oct 20, 2021**\n",
"\n",
"Copyright 1999-2021, [Data Analysis and Machine Learning FYS-STK3155/FYS4155](http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html). Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -67,7 +67,7 @@
"also compare your own results with those that can be obtained using\n",
"for example **Scikit-Learn**'s various SGD options. Discuss your\n",
"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",
"the learning rate $\\eta$. Discuss your results.\n",
"\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",
doc/Projects/2021/Project2/ipynb/ipynb-Project2-src.tar.gz
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doc/Projects/2021/Project2/pdf/Project2.p.tex
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@@ -149,7 +149,7 @@ Project 2 on Machine Learning, deadline November 15 (Midnight)
% --- begin date ---
\begin{center}
Oct 12, 2021
Oct 20, 2021
\end{center}
% --- end date ---
@@ -211,7 +211,7 @@ epochs as well as algorithm for scaling the learning rate. You can
also compare your own results with those that can be obtained using
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.
the learning rate $\eta$. Discuss your results.
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.
doc/Projects/2021/Project2/pdf/Project2.pdf
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doc/Projects/2021/Project2/pdf/Project2.tex
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@@ -123,7 +123,7 @@ Project 2 on Machine Learning, deadline November 15 (Midnight)
% --- begin date ---
\begin{center}
Oct 12, 2021
Oct 20, 2021
\end{center}
% --- end date ---
@@ -185,7 +185,7 @@ epochs as well as algorithm for scaling the learning rate. You can
also compare your own results with those that can be obtained using
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.
the learning rate $\eta$. Discuss your results.
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.
doc/src/Projects/2021/Project2/Project2.do.txt
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@@ -49,7 +49,7 @@ epochs as well as algorithm for scaling the learning rate. You can
also compare your own results with those that can be obtained using
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.
the learning rate $\eta$. Discuss your results.
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.