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Adding test data for CM20315 coursework

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@@ -5,10 +5,10 @@ To be published by MIT Press.
 
 <h2> Download draft PDF </h2>
 
-<a href="https://github.com/udlbook/udlbook/releases/download/v0.2.1/UnderstandingDeepLearning_07_10_22_C.pdf">Draft PDF Chapters 2-13</a><br> 2022-10-07. CC-BY-NC-ND license
+<a href="https://github.com/udlbook/udlbook/releases/download/v0.3.0/UnderstandingDeepLearning_14_12_22.pdf">Draft PDF Chapters 1-16</a><br> 2022-12-14. CC-BY-NC-ND license
 <br>
 <ul>
-	<li> Chapters 1,14-19 (coming Jan 2nd, 2023)
+	<li> Chapters 17-20 (coming soon)
 	<li> Report errata via <a href="https://github.com/udlbook/udlbook/issues">github</a> or contact me directly at udlbookmail@gmail.com
 	<li> Follow me on <a href="https://twitter.com/SimonPrinceAI">Twitter</a> or <a href="https://www.linkedin.com/in/simon-prince-615bb9165/">LinkedIn</a> for updates.
 </ul>
@@ -29,9 +29,9 @@ To be published by MIT Press.
 	<li>  Chapter 12 - Transformers  
 	<li>  Chapter 13 - Graph neural networks 
 	<li>  Chapter 14 - Unsupervised learning
-	<li>  Chapter 15 - Variational auto-encoders
+	<li>  Chapter 15 - Generative adversarial networks
 	<li>  Chapter 16 - Normalizing flows
-	<li>  Chapter 17 - Generative adversarial networks
+	<li>  Chapter 17 - Variational auto-encoders
 	<li>  Chapter 18 - Diffusion models
 	<li>  Chapter 19 - Deep reinforcement learning
 	<li>  Chapter 20 - Why does deep learning work?
@@ -65,9 +65,9 @@ Citation:
 	<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 - Unsupervised learning:  Slides / Notebooks / PDF Figures / Powerpoint Figures
-	<li>  Chapter 15 - Variational auto-encoders:  Slides / Notebooks / PDF Figures / PowerPoint Figures
+	<li>  Chapter 15 - Generative adversarial networks:  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 17 - Variational auto-encoders:  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