update on jupyter-book

doc/LectureNotes/_build/html/chapter1.html

@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
    Exercises week 38
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week38.html">
+   Week 38: Logistic Regression and Optimization
+  </a>
+ </li>
 </ul>
 <p aria-level="2" class="caption" role="heading">
  <span class="caption-text">
@@ -1021,11 +1026,11 @@ example of the functionality of <strong>Scikit-Learn</strong>.</p>
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 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The intercept alpha: 
- [1.99194201]
+ [1.95647867]
 Coefficient beta : 
- [[4.85108001]]
-Mean squared error: 0.28
-Variance score: 0.87
+ [[5.05401912]]
+Mean squared error: 0.25
+Variance score: 0.90
 Mean squared log error: 0.01
 Mean absolute error: 0.43
 </pre></div>
@@ -1127,7 +1132,7 @@ 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>0.005
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.004999999999999991
 </pre></div>
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 </div>