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Morten Hjorth-Jensen
2024-08-26 13:07:33 +02:00
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doc/LectureNotes/_build/html/_sources/week35.ipynb
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@@ -1117,6 +1117,7 @@ doconce format html week35.do.txt --no_mako -->
<h3>Reading recommendations:<a class="headerlink" href="#reading-recommendations" title="Permalink to this headline"></a></h3>
<ol class="simple">
<li><p>These lecture notes</p></li>
<li><p><a class="reference external" href="https://youtu.be/VKakN-e4aUA">Video of lecture</a></p></li>
<li><p>Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)</p></li>
<li><p>Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.</p></li>
<li><p>For exercise 1 of week 35, the book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at URL”<a class="reference external" href="https://mml-book.github.io/">https://mml-book.github.io/</a>” (section 5.5 on derivatives) is very useful for exercise 1 this coming week.</p></li>
@@ -1592,7 +1593,7 @@ 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>0.9935551267466322
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.995597266739957
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@@ -1609,7 +1610,7 @@ 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>0.010490200097725966
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.007984802498580442
</pre></div>
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@@ -1624,23 +1625,23 @@ 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>[0.0154132 0.05341376 0.03110433 0.00569304 0.00506878 0.0148722
0.08128265 0.02279801 0.02093685 0.06532712 0.04539538 0.03257064
0.02869709 0.08842405 0.05211842 0.07932386 0.02115366 0.04175792
0.01811045 0.06077967 0.04565093 0.03645765 0.06066397 0.00485256
0.01797226 0.02614999 0.01806634 0.02108223 0.0066334 0.05980771
0.04400708 0.00630419 0.1383885 0.00044903 0.00641337 0.03058676
0.00976807 0.02733896 0.05637138 0.02881541 0.0163562 0.03518305
0.02460355 0.00280363 0.0044697 0.01549726 0.01692802 0.01292378
0.03745017 0.02810512 0.02286429 0.02165757 0.02666129 0.00779268
0.03131926 0.02339588 0.00461151 0.01516054 0.04771954 0.04964381
0.00310577 0.07038555 0.02244542 0.00549812 0.03432913 0.02357982
0.03663294 0.02518542 0.04143717 0.03388678 0.00780776 0.00132783
0.02747654 0.04561434 0.00158754 0.02174822 0.03353115 0.00624452
0.02947717 0.03378293 0.02430551 0.00728374 0.01379422 0.01427509
0.04598223 0.0442411 0.07660514 0.03523894 0.05599355 0.08451695
0.01631541 0.0314934 0.00085946 0.0174912 0.04138379 0.02711384
0.06866315 0.01900861 0.03389626 0.00703161]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.00320321 0.00245758 0.05081566 0.0019973 0.00120436 0.0178182
0.00900007 0.00408859 0.00048748 0.00486475 0.01992842 0.00354541
0.02097611 0.02124814 0.05100557 0.03342039 0.01004759 0.03045198
0.01334173 0.02570599 0.01152362 0.0055851 0.01331589 0.00670173
0.00111016 0.00674891 0.02190496 0.01318991 0.01012542 0.00536771
0.01056529 0.03104011 0.02194347 0.00570653 0.02260804 0.00449913
0.01584232 0.00856436 0.01271496 0.02217472 0.00201728 0.00743189
0.03465345 0.01961887 0.00071763 0.05253796 0.00713411 0.02597965
0.00044036 0.04210928 0.04276324 0.01934278 0.03413554 0.03117346
0.01696872 0.0103092 0.05179687 0.03101773 0.00419078 0.04059438
0.0681795 0.02798066 0.01269201 0.00019982 0.00546406 0.01333519
0.00925952 0.06136355 0.05846851 0.03697739 0.04446751 0.0707621
0.02017735 0.01044641 0.06713371 0.01791265 0.05612456 0.02375823
0.0050523 0.03568016 0.00771754 0.01959569 0.00679037 0.01420455
0.09201618 0.01115073 0.00372262 0.03688621 0.05250129 0.00520339
0.00423753 0.00267063 0.0575829 0.00228698 0.00253137 0.0280728
0.01264352 0.01788241 0.07061848 0.00521152]
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@@ -1709,15 +1710,15 @@ 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>[ 1.99523653 0.19571155 3.70792235 2.56288447 -1.52977457]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 2.032142 0.27650047 3.22178183 2.62592637 -1.1157376 ]
Training R2
0.9957793953467874
0.994910550643694
Training MSE
0.009780101492052846
0.011016261190269798
Test R2
0.9948937588794076
0.9949909770903465
Test MSE
0.007661225358276448
0.007328424743352142
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