book update

doc/LectureNotes/_build/html/chapter10.html

@@ -57,6 +57,7 @@
     <script async="async" src="_static/sphinx-thebe.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
+    <link rel="next" title="3. Solving Differential Equations with Deep Learning" href="chapter11.html" />
     <link rel="prev" title="1. Neural networks" href="chapter9.html" />
 
     <meta name="viewport" content="width=device-width, initial-scale=1" />
@@ -127,6 +128,11 @@
    1. Elements of Probability Theory and Statistical Data Analysis
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="linalg.html">
+   2. Linear Algebra, Handling of Arrays and more Python Features
+  </a>
+ </li>
 </ul>
 <p class="caption collapsible-parent">
  <span class="caption-text">
@@ -205,6 +211,11 @@
    2. Building a Feed Forward Neural Network
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter11.html">
+   3. Solving Differential Equations  with Deep Learning
+  </a>
+ </li>
 </ul>
 
 </nav> <!-- To handle the deprecated key -->
@@ -1860,53 +1871,53 @@ Accuracy score on test set:  0.8388888888888889
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.1
 Lambda =  1.0
 Accuracy score on test set:  0.9055555555555556
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.1
+
+Learning rate  =  0.1
 Lambda =  10.0
 Accuracy score on test set:  0.8666666666666667
-
-Learning rate  =  1.0
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1.0
 Lambda =  1e-05
 Accuracy score on test set:  0.09166666666666666
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1.0
+
+Learning rate  =  1.0
 Lambda =  0.0001
 Accuracy score on test set:  0.11944444444444445
-
-Learning rate  =  1.0
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1.0
 Lambda =  0.001
 Accuracy score on test set:  0.1361111111111111
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1.0
+
+Learning rate  =  1.0
 Lambda =  0.01
 Accuracy score on test set:  0.1527777777777778
-
-Learning rate  =  1.0
-Lambda =  0.1
-Accuracy score on test set:  0.16666666666666666
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1.0
-Lambda =  1.0
-Accuracy score on test set:  0.1111111111111111
+Lambda =  0.1
+Accuracy score on test set:  0.16666666666666666
 
 Learning rate  =  1.0
+Lambda =  1.0
+Accuracy score on test set:  0.1111111111111111
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1.0
 Lambda =  10.0
 Accuracy score on test set:  0.05
 
 Learning rate  =  10.0
 Lambda =  1e-05
 Accuracy score on test set:  0.08888888888888889
+
+Learning rate  =  10.0
+Lambda =  0.0001
+Accuracy score on test set:  0.08611111111111111
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  10.0
-Lambda =  0.0001
-Accuracy score on test set:  0.08611111111111111
-
-Learning rate  =  10.0
 Lambda =  0.001
 Accuracy score on test set:  0.08888888888888889
 
@@ -2624,6 +2635,7 @@ features).</p>
         <div class='prev-next-bottom'>
             
     <a class='left-prev' id="prev-link" href="chapter9.html" title="previous page"><span class="section-number">1. </span>Neural networks</a>
+    <a class='right-next' id="next-link" href="chapter11.html" title="next page"><span class="section-number">3. </span>Solving Differential Equations  with Deep Learning</a>
 
         </div>