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Morten Hjorth-Jensen
2023-11-08 15:57:33 +01:00
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commit 3b9f0dbb29
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doc/LectureNotes/_build/html/linalg.html
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@@ -343,6 +343,16 @@ const thebe_selector_output = ".output, .cell_output"
Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="week44.html">
Week 44, Convolutional Neural Networks (CNN)
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="week45.html">
Week 45, Recurrent Neural Networks
</a>
</li>
</ul>
<p aria-level="2" class="caption" role="heading">
<span class="caption-text">
@@ -663,9 +673,8 @@ matrices and vectors.</p>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[-8.18243276e-01 -1.33525471e+00 -8.98409646e-01 -7.24434901e-01
2.27832584e+00 -8.78192446e-01 -1.35539164e-03 -7.36097055e-01
-1.06125720e+00 2.86376300e+00]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 0.44079937 -0.14839786 -1.00862798 -0.22996417 0.53459992 0.28570701
-0.40043644 0.43989497 -0.27463692 -0.17644873]
</pre></div>
</div>
</div>
@@ -886,36 +895,26 @@ as (recall that we user lowercase letters for vectors and uppercase letters for
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[3.88960586e-01 2.54050804e-02 9.61246573e-01 4.42642980e-01
4.99344165e-01 8.87477172e-01 2.94785447e-01 4.80006130e-01
8.05206787e-02 9.27743488e-01]
[9.00892604e-01 5.95597666e-01 5.92327822e-01 4.71979573e-01
2.92123586e-01 1.36128659e-01 7.83917436e-01 1.19154013e-01
7.92576742e-01 6.87338811e-01]
[5.40660618e-01 1.11628185e-01 9.46904874e-01 7.61050884e-01
5.94782102e-01 6.61949985e-03 3.01329488e-01 4.26660718e-01
2.07824586e-02 5.75821358e-01]
[7.16539406e-01 8.20261698e-01 5.70328587e-01 5.03331552e-01
9.26331821e-01 6.78989003e-01 9.53683592e-01 1.41471556e-01
3.17934657e-01 2.93828592e-01]
[1.22354860e-01 4.77805840e-01 9.56878976e-01 2.21706540e-01
8.57223110e-01 2.55043852e-01 7.60863613e-01 4.61669885e-01
5.31290061e-01 7.60381656e-01]
[1.68685905e-01 1.26113838e-01 8.15301931e-01 4.38511237e-01
4.42633236e-01 7.28595266e-01 3.30571694e-01 3.15593134e-01
8.22979294e-01 7.32441343e-01]
[5.43608740e-01 2.76181397e-01 6.66722647e-01 7.35434042e-01
2.44020145e-01 2.15352815e-01 1.91487049e-01 6.11137061e-01
2.79167312e-01 2.27230624e-01]
[8.97033646e-02 1.88667234e-01 1.80076024e-01 8.65728698e-01
2.79254918e-01 3.33457718e-01 9.52626194e-01 8.75240336e-01
7.76059447e-01 3.52565509e-01]
[8.49556373e-03 1.50925722e-04 8.98413020e-01 7.49186362e-01
1.90602444e-01 4.79877535e-01 6.10615323e-01 7.78031111e-02
2.39856058e-01 3.20554718e-01]
[2.63843494e-01 1.46306318e-01 7.29933720e-01 5.27826845e-01
1.33857909e-01 3.50359169e-01 4.20020918e-01 8.58581665e-03
5.54378734e-02 8.68351374e-01]]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[0.51727541 0.09312344 0.68284332 0.10399743 0.11110246 0.17604689
0.83716603 0.72598009 0.89142728 0.54640368]
[0.33656494 0.55847112 0.87450434 0.13621148 0.38488879 0.68655761
0.74147751 0.2544422 0.33708747 0.6848187 ]
[0.54213329 0.22197349 0.8702764 0.36674564 0.59816099 0.3225819
0.18856622 0.88595314 0.46132345 0.55685628]
[0.88866133 0.65822169 0.05062537 0.20513942 0.41671085 0.48333258
0.12786653 0.31532451 0.56548318 0.65766777]
[0.55206229 0.11704038 0.2856881 0.26961519 0.13371503 0.26805987
0.34460089 0.09215672 0.53367133 0.34362409]
[0.34642944 0.54617756 0.22752605 0.86192438 0.49901588 0.98157799
0.69802962 0.6513444 0.88050263 0.66725024]
[0.88657125 0.3591093 0.14600426 0.66667985 0.95921115 0.09607524
0.0470705 0.42777999 0.65318467 0.59602968]
[0.37187359 0.6945612 0.7840642 0.78347558 0.60307141 0.95682858
0.99534399 0.1859082 0.4281152 0.11232098]
[0.8459616 0.64874236 0.39148625 0.284499 0.35322418 0.37391132
0.39837199 0.47950427 0.44298022 0.31921368]
[0.89881513 0.0062825 0.19942021 0.86850693 0.90715001 0.3426926
0.98597638 0.71365128 0.79367372 0.86629645]]
</pre></div>
</div>
</div>
@@ -975,13 +974,13 @@ covariance matrix through the <strong>np.linalg.eig()</strong> function.</p>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.07291818479810824
4.313183599076104
-0.05687021620384533
[[ 0.87810129 2.59703606 2.52470105]
[ 2.59703606 8.59883217 6.83603568]
[ 2.52470105 6.83603568 13.87354403]]
[19.25091007 0.06413187 4.03543554]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.10095106250934528
3.73293228266057
-0.589971818845805
[[ 1.44967228 4.20536556 4.14179769]
[ 4.20536556 13.10814421 12.2161908 ]
[ 4.14179769 12.2161908 17.03056169]]
[28.710746 0.0846527 2.79297948]
</pre></div>
</div>
</div>