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+    <div id="app" data-server-rendered="true"><div class="theme-container"><div class="theme-default-content"><h1>404</h1> <blockquote>That's a Four-Oh-Four.</blockquote> <a href="/dl-demystified-course/" class="router-link-active">
       Take me home.
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diff --git a/assets/js/app.ab6b550e.js → assets/js/app.c237c802.js b/assets/js/app.ab6b550e.js → assets/js/app.c237c802.js
diff --git a/index.html b/index.html
diff --git a/slides/00_overview.html b/slides/00_overview.html
@@ -18,7 +18,9 @@
     <div class="reveal">
       <div class="slides"><section  data-markdown><script type="text/template">
 
-# RSES: Introduction to Deep Learning Course
+# Deep Learning Demystified
+## Foundations for Non-Computer Scientist
+
 
 Twin Karmakharm
 
@@ -33,14 +35,13 @@
     * Grant support
 * Software optimisation, GPU and HPC
 * Training, outreach and education activities
-* Led by Dr. Paul Richmond
-* Visit us at [https://rse.shef.ac.uk](https://rse.shef.ac.uk)
-* [Apply for up to 12 days of RSE time on AI/HPC related project](https://rse.shef.ac.uk/collaboration/tier2)</script></section><section  data-markdown><script type="text/template">
+* Led by Dr. Romain Thomas
+* Visit us at [https://rse.shef.ac.uk](https://rse.shef.ac.uk)</script></section><section  data-markdown><script type="text/template">
 ### Course Materials
 
-All course materials can be found at:
+Course materials can be found at:
 
-[https://rses-dl-course.github.io/](https://rses-dl-course.github.io/)
+[https://rse.shef.ac.uk/dl-demystified-course](https://rse.shef.ac.uk/dl-demystified-course)
 
 </script></section><section  data-markdown><script type="text/template"><!-- .slide: data-visibility="hidden" -->
 ### Course Schedule
@@ -29618,7 +29619,7 @@
 `Shift+Enter` to run code, or use the 'Play' button on top-left 
 
 `Ctrl+Space` to get code suggestion
-</script></section><section  data-markdown><script type="text/template">
+</script></section><section  data-markdown><script type="text/template"><!-- .slide: data-visibility="hidden" -->
 
 ### Practical Labs Using Jupyter Notebooks
 A platform to run lab code interactively

diff --git a/slides/01_introduction_to_deep_learning.html b/slides/01_introduction_to_deep_learning.html
@@ -10997,7 +10997,7 @@
 * Matlab - Deep Learning Toolbox
 
 Or import from python
-* 'keras' package for R - Used for R version of the course
+* 'keras' package for R
 </script></section><section  data-markdown><script type="text/template">
 ### Keras (Tensorflow)
 

diff --git a/slides/02_neural_networks.html b/slides/02_neural_networks.html
@@ -5745,7 +5745,7 @@
 </script></section><section  data-markdown><script type="text/template">
 ### Error and Loss
 
-We need a way good way to quantify how far our prediction is from our goal.
+We need a good way to quantify how far our prediction is from our goal.
 
 <svg xmlns:ns3="http://www.iki.fi/pav/software/textext/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:svg="http://www.w3.org/2000/svg" xmlns="http://www.w3.org/2000/svg" xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd" xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape" width="745.64404" height="349.65405" version="1.1" id="svg3340" sodipodi:docname="neuralnetwork-initialisation.svg" inkscape:version="1.0.2 (1.0.2+r75+1)" style="background: white; width: 80%; margin-top: 1em">
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@@ -12398,7 +12398,7 @@
 
 How do we know the 'optimal' learning rate?
 
-We won't know until actually training the network! <!-- .element: class="fragment" -->
+We won't know until we train the network! <!-- .element: class="fragment" -->
 
 Start with the default values provided. <!-- .element: class="fragment" -->
 
@@ -19599,7 +19599,7 @@
 1. Calculate loss - compare prediction with target
 1. Back propagation - find gradients for each weight and bias
 1. Repeat for all samples
-1. Average gradients of weights and bias then update <!-- .element: class="fragment" -->
+1. Average the delta of weights and bias then update <!-- .element: class="fragment" -->
 1. Start again from 1 <!-- .element: class="fragment" -->
 </script></section><section  data-markdown><script type="text/template">
 ### Stochastic gradient descent

diff --git a/slides/index.html b/slides/index.html
@@ -46,7 +46,7 @@
     </ul>
 
     <p>
-      Last update: 2024-02-20T23:11:38.416Z
+      Last update: 2024-02-22T01:19:43.465Z
     </p>
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