From e182339f29dddeb1546bdd1f4e6c0b3367443f3b Mon Sep 17 00:00:00 2001
From: udlbook <110402648+udlbook@users.noreply.github.com>
Date: Fri, 18 Nov 2022 14:30:12 +0000
Subject: [PATCH] Update index.html

---
 index.html | 29 ++++++++++++++++-------------
 1 file changed, 16 insertions(+), 13 deletions(-)

diff --git a/index.html b/index.html
index da46157..f2a5155 100644
--- a/index.html
+++ b/index.html
@@ -24,15 +24,17 @@ To be published by MIT Press.
 	<li>  Chapter 7 - Gradients and initialization 
 	<li>  Chapter 8 - Measuring performance 
 	<li>  Chapter 9 - Regularization 
+	<li>  Chapter 10 - Convolutional netowrks
 	<li>  Chapter 11 - Residual networks
 	<li>  Chapter 12 - Transformers  
 	<li>  Chapter 13 - Graph neural networks 
-	<li>  Chapter 14 - Variational auto-encoders
-	<li>  Chapter 15 - Normalizing flows
-	<li>  Chapter 16 - Generative adversarial networks
-	<li>  Chapter 17 - Diffusion models
-	<li>  Chapter 18 - Deep reinforcement learning
-	<li>  Chapter 19 - Why does deep learning work?
+	<li>  Chapter 14 - Unsupervised learning
+	<li>  Chapter 15 - Variational auto-encoders
+	<li>  Chapter 16 - Normalizing flows
+	<li>  Chapter 17 - Generative adversarial networks
+	<li>  Chapter 18 - Diffusion models
+	<li>  Chapter 19 - Deep reinforcement learning
+	<li>  Chapter 20 - Why does deep learning work?
 </ul>
 
 <br>
@@ -58,14 +60,15 @@ Citation:
 	<li>  Chapter 7 - Gradients and initialization:  Slides / Notebooks /  <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap7PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap7.pptx">PowerPoint Figures</a> 
 	<li>  Chapter 8 - Measuring performance:  Slides / Notebooks /  <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap8PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap8.pptx">PowerPoint Figures</a> 
 	<li>  Chapter 9 - Regularization: Slides / Notebooks /  <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap9PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap9.pptx">PowerPoint Figures</a> 
-	<li>  Chapter 10 - Convolutional nets:  Slides / Notebooks / <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap10PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap10.pptx">PowerPoint Figures</a> 
+	<li>  Chapter 10 - Convolutional networks:  Slides / Notebooks / <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap10PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap10.pptx">PowerPoint Figures</a> 
 	<li>  Chapter 11 - Residual networks: Slides / Notebooks /  <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap11PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap11.pptx">PowerPoint Figures</a> 
 	<li>  Chapter 12 - Transformers:  Slides / Notebooks / <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap12PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap12.pptx">PowerPoint Figures</a> 
 	<li>  Chapter 13 - Graph neural networks:  Slides / Notebooks /   <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap13PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap13.pptx">PowerPoint Figures</a> 
-	<li>  Chapter 14 - Variational auto-encoders:  Slides / Notebooks / PDF Figures / PowerPoint Figures
-	<li>  Chapter 15 - Normalizing flows:  Slides / Notebooks / PDF Figures / PowerPoint Figures
-	<li>  Chapter 16 - Generative adversarial networks:  Slides / Notebooks / PDF Figures / PowerPoint Figures
-	<li>  Chapter 17 - Diffusion models: Slides / Notebooks / PDF Figures / PowerPoint Figures
-	<li>  Chapter 18 - Deep reinforcement learning:  Slides / Notebooks / PDF Figures / PowerPoint Figures
-	<li>  Chapter 19 - Why does deep learning work?:  Slides / Notebooks / PDF Figures / PowerPoint Figures
+	<li>  Chapter 14 - Unsupervised learning:  Slides / Notebooks / PDF Figures / Powerpoint Figures
+	<li>  Chapter 15 - Variational auto-encoders:  Slides / Notebooks / PDF Figures / PowerPoint Figures
+	<li>  Chapter 16 - Normalizing flows:  Slides / Notebooks / PDF Figures / PowerPoint Figures
+	<li>  Chapter 17 - Generative adversarial networks:  Slides / Notebooks / PDF Figures / PowerPoint Figures
+	<li>  Chapter 18 - Diffusion models: Slides / Notebooks / PDF Figures / PowerPoint Figures
+	<li>  Chapter 19 - Deep reinforcement learning:  Slides / Notebooks / PDF Figures / PowerPoint Figures
+	<li>  Chapter 20 - Why does deep learning work?:  Slides / Notebooks / PDF Figures / PowerPoint Figures
 </ul>