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adding dft slides

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Morten Hjorth-Jensen
2023-10-25 15:38:49 +02:00
parent 248fa398fc
commit 9587aff02c
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doc/LectureNotes/_build/html/week35.html
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@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
Week 42 Constructing a Neural Network code with introduction to Tensor flow
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="exercisesweek43.html">
Exercises weeks 43 and 44
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="week43.html">
Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
</a>
</li>
</ul>
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@@ -1696,7 +1706,7 @@ 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>0.995840825550726
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9955273625597437
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@@ -1713,7 +1723,7 @@ 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>0.007607459165915922
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.008900933315885705
</pre></div>
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@@ -1728,23 +1738,23 @@ 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>[0.00552246 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
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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]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.00643899 0.04246989 0.0607062 0.02997344 0.0011878 0.00123457
0.00324986 0.03285652 0.01028728 0.01571866 0.03940381 0.0814985
0.038844 0.01828593 0.03967758 0.00303693 0.02227466 0.02702328
0.00026861 0.00883798 0.02876697 0.00472251 0.03141454 0.02911162
0.02387339 0.00585113 0.00110716 0.00587564 0.00693821 0.00604105
0.00804985 0.02058094 0.01151984 0.01782721 0.0286851 0.08874631
0.01678538 0.0065912 0.03611471 0.02706508 0.00313125 0.04977093
0.00415289 0.02760079 0.00518122 0.00628874 0.00739489 0.01619456
0.00996972 0.02210753 0.02030107 0.02100763 0.04699527 0.01512934
0.00717079 0.01784714 0.01095703 0.01281486 0.0231703 0.04482932
0.00287871 0.0565419 0.04028659 0.03102525 0.01617722 0.0271761
0.01736502 0.03394827 0.00328494 0.05750876 0.0059888 0.01915888
0.01423609 0.01024227 0.03660869 0.01012951 0.00534938 0.03375068
0.02699539 0.04083439 0.04965227 0.00565625 0.02250553 0.00893027
0.02244755 0.00741987 0.00189101 0.02042476 0.02036545 0.06362348
0.03145163 0.02987833 0.07393685 0.0033575 0.02791218 0.00214832
0.0111154 0.01344581 0.00368581 0.01436601]
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@@ -1813,15 +1823,15 @@ 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>[ 2.04899609 -0.34193915 5.64527549 -0.69997503 0.31290684]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 2.09851217 -1.48209629 10.27096183 -6.79998516 2.87206824]
Training R2
0.9952222065466447
0.9957273060382023
Training MSE
0.008897354602673473
0.010053880703541525
Test R2
0.9915165982451293
0.9888005551376943
Test MSE
0.009442796383765939
0.008043926731954223
</pre></div>
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