diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle
index 12fe57055..4e36b16e2 100644
Binary files a/doc/LectureNotes/_build/.doctrees/environment.pickle and b/doc/LectureNotes/_build/.doctrees/environment.pickle differ
diff --git a/doc/LectureNotes/_build/.doctrees/schedule.doctree b/doc/LectureNotes/_build/.doctrees/schedule.doctree
index 2a0bd7dcd..e5fa0bf5c 100644
Binary files a/doc/LectureNotes/_build/.doctrees/schedule.doctree and b/doc/LectureNotes/_build/.doctrees/schedule.doctree differ
diff --git a/doc/LectureNotes/_build/html/_sources/schedule.md b/doc/LectureNotes/_build/html/_sources/schedule.md
index dcba97ae1..dd159bf54 100644
--- a/doc/LectureNotes/_build/html/_sources/schedule.md
+++ b/doc/LectureNotes/_build/html/_sources/schedule.md
@@ -103,7 +103,7 @@ For the reading assignments we use the following abbreviations:
 - Lab Wednesday: Wrap up project 1
 - Lecture Thursday: Stochastic gradient descent, automatic differentiation and start discussion of  feed-forward Neural Network code for regression and classification
   - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober7.mp4?vrtx=view-as-webpage
-- Lecture Friday:  Deep Learning and Neural Networks
+- Lecture Friday:  Deep Learning and Neural Networks: the back propagation algorithm
   - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober8.mp4?vrtx=view-as-webpage 
 - Reading recommendations:
   - See lecture notes for week 40 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
@@ -112,9 +112,10 @@ For the reading assignments we use the following abbreviations:
 
 ###  Week 41 October 11-15
 - Lab Wednesday: Work on project 2
-- Lecture Thursday: Deep learning and Neural Networks
+- Lecture Thursday: Deep learning and Neural Networks, developing a code for Neural Networks
   - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober14.mp4?vrtx=view-as-webpage
-- Lecture Friday: Convolutional Neural Networks, basic elements
+- Lecture Friday: Tensorflow and the mathematics of neural network
+  - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureOctober15.mp4?vrtx=view-as-webpage
 - Reading recommendations:
   - See lecture notes for week 41 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
   - For neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications
diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html
index 3d10e2378..cc3267c68 100644
--- a/doc/LectureNotes/_build/html/schedule.html
+++ b/doc/LectureNotes/_build/html/schedule.html
@@ -540,7 +540,7 @@
 <li><p>Video of Lecture at <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober7.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober7.mp4?vrtx=view-as-webpage</a></p></li>
 </ul>
 </li>
-<li><p>Lecture Friday:  Deep Learning and Neural Networks</p>
+<li><p>Lecture Friday:  Deep Learning and Neural Networks: the back propagation algorithm</p>
 <ul>
 <li><p>Video of Lecture at <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober8.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober8.mp4?vrtx=view-as-webpage</a></p></li>
 </ul>
@@ -558,12 +558,16 @@
 <h3>Week 41 October 11-15<a class="headerlink" href="#week-41-october-11-15" title="Permalink to this headline">¶</a></h3>
 <ul class="simple">
 <li><p>Lab Wednesday: Work on project 2</p></li>
-<li><p>Lecture Thursday: Deep learning and Neural Networks</p>
+<li><p>Lecture Thursday: Deep learning and Neural Networks, developing a code for Neural Networks</p>
 <ul>
 <li><p>Video of Lecture at <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober14.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober14.mp4?vrtx=view-as-webpage</a></p></li>
 </ul>
 </li>
-<li><p>Lecture Friday: Convolutional Neural Networks, basic elements</p></li>
+<li><p>Lecture Friday: Tensorflow and the mathematics of neural network</p>
+<ul>
+<li><p>Video of Lecture at <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureOctober15.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureOctober15.mp4?vrtx=view-as-webpage</a></p></li>
+</ul>
+</li>
 <li><p>Reading recommendations:</p>
 <ul>
 <li><p>See lecture notes for week 41 at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>.</p></li>
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index f6915c107..23c954b2e 100644
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class=\"section-number\">12. </span>Clustering Analysis","<span class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","Content in Jupyter 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class=\"section-number\">12. </span>Clustering Analysis","<span class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","Content in Jupyter Book","Applied Data Analysis and Machine Learning, FYS-STK3155/4155 at the University of Oslo, Norway","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and 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\ No newline at end of file
diff --git a/doc/LectureNotes/content.md b/doc/LectureNotes/content.md
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--- a/doc/LectureNotes/content.md
+++ /dev/null
@@ -1,5 +0,0 @@
-Content in Jupyter Book
-=======================
-
-There are many ways to write content in Jupyter Book. This short section
-covers a few tips for how to do so.