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Morten Hjorth-Jensen
2023-10-15 21:55:49 +02:00
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commit edf33b439c
171 changed files with 18722 additions and 1691 deletions
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@@ -313,6 +313,26 @@ const thebe_selector_output = ".output, .cell_output"
Week 40: Gradient descent methods (continued) and start Neural networks
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<a class="reference internal" href="exercisesweek41.html">
Exercises week 41
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Week 41 Neural networks and constructing a neural network code
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<a class="reference internal" href="exercisesweek42.html">
Exercises week 42
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<a class="reference internal" href="week42.html">
Week 42 Constructing a Neural Network code with introduction to Tensor flow
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@@ -325,6 +345,11 @@ const thebe_selector_output = ".output, .cell_output"
Project 1 on Machine Learning, deadline October 9 (midnight), 2023
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Project 2 on Machine Learning, deadline November 13 (Midnight)
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@@ -1671,7 +1696,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9963311287748658
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.995840825550726
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@@ -1688,7 +1713,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
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<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.007891914573161948
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.007607459165915922
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@@ -1703,23 +1728,23 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
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<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.00712321 0.05126901 0.00172452 0.00104613 0.01477821 0.06642248
0.04547353 0.0207306 0.01552289 0.01492 0.0200568 0.01097423
0.02400359 0.04111096 0.02126208 0.00799998 0.00976647 0.0447389
0.0006527 0.02468681 0.03543039 0.00627535 0.04259402 0.02635835
0.04362755 0.00478655 0.01989299 0.01508632 0.04372783 0.00324969
0.01508966 0.08185315 0.01214101 0.01130932 0.00186362 0.02809859
0.00480371 0.08481871 0.01881546 0.04314342 0.11022363 0.01288591
0.03370315 0.00858536 0.00202679 0.00229911 0.00446979 0.0496375
0.00426531 0.02593026 0.03063575 0.03119091 0.05459089 0.01916913
0.00183869 0.00015239 0.01347636 0.01267006 0.02702978 0.02944425
0.01424197 0.00354492 0.06026294 0.00148709 0.02061026 0.03616508
0.03543958 0.02079171 0.00595615 0.01765474 0.01268892 0.00225484
0.01135167 0.03894328 0.04426647 0.03717939 0.01502518 0.03195835
0.00474485 0.06331463 0.01671556 0.01762067 0.00219624 0.07358383
0.01038358 0.0002364 0.04669463 0.00048325 0.00012934 0.01529503
0.0093869 0.00952586 0.0154222 0.01463052 0.0296969 0.01372375
0.02536494 0.00472549 0.04225015 0.04690007]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.00552246 0.00115669 0.02017377 0.01896127 0.01389847 0.01591021
0.02816083 0.0100949 0.02757522 0.00182747 0.00584432 0.02081274
0.01365363 0.0191717 0.01068907 0.02763182 0.02950229 0.02485679
0.01456159 0.00644939 0.0297291 0.08272096 0.01335857 0.00825399
0.04478101 0.05834444 0.04198166 0.034985 0.00562524 0.01524072
0.01529708 0.02083512 0.01161357 0.01708691 0.02279888 0.02912421
0.00076617 0.02329285 0.00626773 0.01054509 0.00460405 0.01476097
0.0036718 0.00569405 0.07804489 0.03894873 0.02103178 0.00726135
0.00353575 0.00857028 0.00923278 0.01616709 0.02881357 0.00550379
0.02942218 0.00946636 0.03982972 0.0149713 0.04103307 0.05526765
0.00463639 0.00254359 0.00915433 0.02588522 0.00090992 0.00739382
0.02075115 0.024632 0.00115506 0.01963203 0.00086063 0.01580414
0.01059601 0.03376827 0.02745507 0.02109939 0.05977068 0.04662395
0.00283853 0.03903968 0.0001225 0.02385515 0.02089297 0.04214702
0.01289962 0.00798188 0.04746791 0.04822955 0.02066371 0.01045774
0.01164198 0.03633213 0.00183398 0.0105301 0.00880924 0.015244
0.01596986 0.01176096 0.01448147 0.00610607]
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@@ -1788,15 +1813,15 @@ but now splitting the data into a training set and a test set.</p>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 1.95558642 0.77265448 2.8784267 1.83384573 -0.41219619]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 2.04899609 -0.34193915 5.64527549 -0.69997503 0.31290684]
Training R2
0.9969332511584248
0.9952222065466447
Training MSE
0.006829400694106674
0.008897354602673473
Test R2
0.9950597269547777
0.9915165982451293
Test MSE
0.011917343246903285
0.009442796383765939
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