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@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
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Week 39: Optimization and Gradient Methods
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="week40.html">
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Week 40: Gradient descent methods (continued) and start Neural networks
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="exercisesweek41.html">
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Exercises week 41
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="week41.html">
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Week 41 Neural networks and constructing a neural network code
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</a>
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</li>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
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Project 1 on Machine Learning, deadline October 7 (midnight), 2024
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="project2.html">
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Project 2 on Machine Learning, deadline November 4 (Midnight)
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</a>
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</li>
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</ul>
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</div>
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@@ -623,8 +643,8 @@ matrices and vectors.</p>
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</div>
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</div>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 0.69465386 1.75956617 -0.23303727 -0.53125507 1.34598722 -1.09928714
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1.37013105 0.79898903 -0.23663482 0.99427512]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 0.03563709 0.57852915 0.83220985 1.27108866 -0.3587467 -0.38713573
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-0.09584387 0.5223261 1.7663967 0.94027059]
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</pre></div>
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</div>
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</div>
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@@ -845,26 +865,26 @@ as (recall that we user lowercase letters for vectors and uppercase letters for
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</div>
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</div>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[0.81947005 0.93619369 0.37449168 0.91933003 0.76855921 0.41820116
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0.96853248 0.60375434 0.96015381 0.30269539]
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[0.46086448 0.16777789 0.9930742 0.10837392 0.69089532 0.94221383
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0.53629564 0.50327198 0.33734605 0.04757138]
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[0.51069279 0.12363332 0.79171202 0.16791183 0.62617788 0.9288904
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0.85112594 0.86519139 0.61192712 0.90842732]
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[0.82551764 0.67524588 0.02175561 0.1118933 0.42575338 0.45731379
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0.61069681 0.40184681 0.18702469 0.71838601]
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[0.68655456 0.11747908 0.28253033 0.4591127 0.68072161 0.59372982
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0.95343966 0.24780663 0.98740373 0.06808421]
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[0.99512017 0.14828178 0.02354386 0.90860768 0.891715 0.39039235
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0.48151166 0.43563433 0.52657934 0.73176319]
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[0.77030637 0.00676256 0.37454707 0.5076963 0.51937727 0.46065811
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0.65917558 0.72962885 0.99370678 0.92341148]
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[0.16292908 0.17214545 0.44995924 0.20367355 0.64885265 0.34225662
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0.4215795 0.27933134 0.02552966 0.62908496]
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[0.8084934 0.51364117 0.4937346 0.05296475 0.69247718 0.56783103
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0.85276538 0.52635761 0.96461948 0.67374815]
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[0.02137508 0.03177331 0.78186404 0.33096549 0.8423144 0.07745579
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0.4619526 0.61414743 0.38460453 0.51928402]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[0.79010601 0.87637891 0.68824222 0.4636751 0.50100007 0.22715479
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0.17865868 0.90903158 0.5736973 0.96961052]
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[0.57293306 0.96660465 0.65525178 0.52480767 0.70159137 0.30894285
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0.11675128 0.60321647 0.68281739 0.64952115]
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[0.21833102 0.74761815 0.52789388 0.27530242 0.69463406 0.89961861
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0.91864509 0.41794469 0.27403356 0.11416086]
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[0.77596142 0.20224027 0.68830164 0.50895251 0.83741078 0.71957514
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0.78945959 0.94466211 0.06443054 0.29474356]
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[0.39914635 0.22706777 0.23499891 0.9794096 0.33435637 0.28614301
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0.21641173 0.16925937 0.79086674 0.41259788]
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[0.70408202 0.57833531 0.01817739 0.64689773 0.71380438 0.69311221
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0.09930135 0.90168941 0.47308061 0.445128 ]
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[0.10100211 0.60575887 0.69824402 0.06423317 0.24582593 0.97235642
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0.21181534 0.72033728 0.77014839 0.13298019]
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[0.25816519 0.81826799 0.19336703 0.34098895 0.10688434 0.34134773
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0.21635399 0.57016227 0.69925648 0.01418766]
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[0.80374623 0.58202531 0.71460518 0.66363129 0.02553865 0.7204561
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0.34704885 0.52927353 0.02631244 0.02944974]
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[0.57080764 0.04516434 0.15388662 0.99458998 0.2765068 0.05148401
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0.82259916 0.05648118 0.14249052 0.96164155]]
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</pre></div>
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</div>
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</div>
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@@ -924,13 +944,15 @@ covariance matrix through the <strong>np.linalg.eig()</strong> function.</p>
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</div>
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</div>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.07218473624441492
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4.346000154268618
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0.09048717285916912
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[[ 1.0974119 3.2131067 3.36880327]
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[ 3.2131067 10.51564682 9.75138064]
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[ 3.36880327 9.75138064 17.26527089]]
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[25.09487358 0.09070064 3.6927554 ]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.22370461988004753
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4.592766658048914
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.7163835161938888
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[[ 1.11935351 3.19945294 3.19001247]
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[ 3.19945294 10.52074297 8.75737136]
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[ 3.19001247 8.75737136 16.29078236]]
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[23.51532469 0.10801706 4.3075371 ]
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</pre></div>
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</div>
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</div>
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