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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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@@ -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.