-
@@ -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 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 useful when plotting the results as function of the learning rate \( \eta \) and the hyper-parameter \( \lambda \) when you use Ridge regression. diff --git a/doc/Projects/2021/Project2/html/Project2-bs.html b/doc/Projects/2021/Project2/html/Project2-bs.html index 3658ea708..8996c18a2 100644 --- a/doc/Projects/2021/Project2/html/Project2-bs.html +++ b/doc/Projects/2021/Project2/html/Project2-bs.html @@ -165,7 +165,7 @@ MathJax.Hub.Config({
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@@ -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 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 useful when plotting the results as function of the learning rate \( \eta \) and the hyper-parameter \( \lambda \) when you use Ridge regression. diff --git a/doc/Projects/2021/Project2/html/Project2.html b/doc/Projects/2021/Project2/html/Project2.html index 1ae046634..775efa135 100644 --- a/doc/Projects/2021/Project2/html/Project2.html +++ b/doc/Projects/2021/Project2/html/Project2.html @@ -123,7 +123,7 @@ MathJax.Hub.Config({
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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. diff --git a/doc/Projects/2021/Project2/ipynb/Project2.ipynb b/doc/Projects/2021/Project2/ipynb/Project2.ipynb index b6ea3d965..9d93e67c6 100644 --- a/doc/Projects/2021/Project2/ipynb/Project2.ipynb +++ b/doc/Projects/2021/Project2/ipynb/Project2.ipynb @@ -10,7 +10,7 @@ " \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", 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 91a0fe134..1084918e1 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 90db3aaec..962c42528 100644 --- a/doc/Projects/2021/Project2/pdf/Project2.p.tex +++ b/doc/Projects/2021/Project2/pdf/Project2.p.tex @@ -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. diff --git a/doc/Projects/2021/Project2/pdf/Project2.pdf b/doc/Projects/2021/Project2/pdf/Project2.pdf index 1cbc3c1c5..9e00459a1 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 13ae9c648..efcaf844a 100644 --- a/doc/Projects/2021/Project2/pdf/Project2.tex +++ b/doc/Projects/2021/Project2/pdf/Project2.tex @@ -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. diff --git a/doc/src/Projects/2021/Project2/Project2.do.txt b/doc/src/Projects/2021/Project2/Project2.do.txt index c09edff79..a7d732300 100644 --- a/doc/src/Projects/2021/Project2/Project2.do.txt +++ b/doc/src/Projects/2021/Project2/Project2.do.txt @@ -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.