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doc/LectureNotes/_build/html/chapter1.html

@@ -298,6 +298,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 38: Logistic Regression and Optimization
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek39.html">
+   Exercises week 39
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week39.html">
+   Week 39: Optimization and  Gradient Methods
+  </a>
+ </li>
 </ul>
 <p aria-level="2" class="caption" role="heading">
  <span class="caption-text">
@@ -1026,13 +1036,13 @@ example of the functionality of <strong>Scikit-Learn</strong>.</p>
 </div>
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 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The intercept alpha: 
- [1.95647867]
+ [1.92622842]
 Coefficient beta : 
- [[5.05401912]]
-Mean squared error: 0.25
+ [[5.21332621]]
+Mean squared error: 0.22
 Variance score: 0.90
 Mean squared log error: 0.01
-Mean absolute error: 0.43
+Mean absolute error: 0.38
 </pre></div>
 </div>
 <img alt="_images/chapter1_19_1.png" src="_images/chapter1_19_1.png" />
@@ -1132,7 +1142,7 @@ a linear <span class="math notranslate nohighlight">\(x\)</span>-dependence we s
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 <img alt="_images/chapter1_33_0.png" src="_images/chapter1_33_0.png" />
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.004999999999999991
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.005
 </pre></div>
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