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small change to project 2

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Morten Hjorth-Jensen
2021-10-12 09:40:05 +02:00
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commit b45f04756e
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doc/src/Projects/2021/Project2/Project2.do.txt
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@@ -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 ===