update first week

doc/pub/week1/html/week1-bs.html

@@ -511,6 +511,8 @@ <h2 id="deep-learning-methods-covered-tentative" class="anchor">Deep learning me
  <li> Feed forward neural networks and its mathematics (NNs)</li>
  <li> Convolutional neural networks (CNNs)</li>
  <li> Recurrent neural networks (RNNs)</li>
+ <li> Graph neural networks</li>
+ <li> Transformers</li>  
  <li> Autoencoders and principal component analysis</li>
 </ol>
 <li> <b>Deep learning, generative methods</b>
@@ -534,6 +536,8 @@ <h2 id="additional-topics-kernel-regression-gaussian-processes-and-bayesian-stat
 variable).
 </p>
 
+<p>We have not made plans for Reinforcement learning, but this can be another option.</p>
+
 <!-- !split -->
 <h2 id="scientific-machine-learning" class="anchor">Scientific Machine Learning </h2>
 

doc/pub/week1/html/week1-reveal.html

@@ -227,7 +227,10 @@ <h2 id="deep-learning-methods-covered-tentative">Deep learning methods covered,
  <p><li> Feed forward neural networks and its mathematics (NNs)</li>
  <p><li> Convolutional neural networks (CNNs)</li>
  <p><li> Recurrent neural networks (RNNs)</li>
- <p><li> Autoencoders and principal component analysis</li>
+ <p><li> Graph neural networks</li>
+ <p><li> Transformers</li>
+
+<p><li> Autoencoders and principal component analysis</li>
 </ol>
 <p>
 <p><li> <b>Deep learning, generative methods</b>
@@ -253,6 +256,8 @@ <h2 id="additional-topics-kernel-regression-gaussian-processes-and-bayesian-stat
 large number of variables and a particular outcome (dependent
 variable).
 </p>
+
+<p>We have not made plans for Reinforcement learning, but this can be another option.</p>
 </section>
 
 <section>

doc/pub/week1/html/week1-solarized.html

@@ -420,6 +420,8 @@ <h2 id="deep-learning-methods-covered-tentative">Deep learning methods covered,
  <li> Feed forward neural networks and its mathematics (NNs)</li>
  <li> Convolutional neural networks (CNNs)</li>
  <li> Recurrent neural networks (RNNs)</li>
+ <li> Graph neural networks</li>
+ <li> Transformers</li>  
  <li> Autoencoders and principal component analysis</li>
 </ol>
 <li> <b>Deep learning, generative methods</b>
@@ -443,6 +445,8 @@ <h2 id="additional-topics-kernel-regression-gaussian-processes-and-bayesian-stat
 variable).
 </p>
 
+<p>We have not made plans for Reinforcement learning, but this can be another option.</p>
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="scientific-machine-learning">Scientific Machine Learning </h2>
 

doc/pub/week1/html/week1.html

@@ -497,6 +497,8 @@ <h2 id="deep-learning-methods-covered-tentative">Deep learning methods covered,
  <li> Feed forward neural networks and its mathematics (NNs)</li>
  <li> Convolutional neural networks (CNNs)</li>
  <li> Recurrent neural networks (RNNs)</li>
+ <li> Graph neural networks</li>
+ <li> Transformers</li>  
  <li> Autoencoders and principal component analysis</li>
 </ol>
 <li> <b>Deep learning, generative methods</b>
@@ -520,6 +522,8 @@ <h2 id="additional-topics-kernel-regression-gaussian-processes-and-bayesian-stat
 variable).
 </p>
 
+<p>We have not made plans for Reinforcement learning, but this can be another option.</p>
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="scientific-machine-learning">Scientific Machine Learning </h2>