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@@ -24,15 +24,17 @@ To be published by MIT Press.
<li> Chapter 7 - Gradients and initialization <li> Chapter 7 - Gradients and initialization
<li> Chapter 8 - Measuring performance <li> Chapter 8 - Measuring performance
<li> Chapter 9 - Regularization <li> Chapter 9 - Regularization
<li> Chapter 10 - Convolutional netowrks
<li> Chapter 11 - Residual networks <li> Chapter 11 - Residual networks
<li> Chapter 12 - Transformers <li> Chapter 12 - Transformers
<li> Chapter 13 - Graph neural networks <li> Chapter 13 - Graph neural networks
<li> Chapter 14 - Variational auto-encoders <li> Chapter 14 - Unsupervised learning
<li> Chapter 15 - Normalizing flows <li> Chapter 15 - Variational auto-encoders
<li> Chapter 16 - Generative adversarial networks <li> Chapter 16 - Normalizing flows
<li> Chapter 17 - Diffusion models <li> Chapter 17 - Generative adversarial networks
<li> Chapter 18 - Deep reinforcement learning <li> Chapter 18 - Diffusion models
<li> Chapter 19 - Why does deep learning work? <li> Chapter 19 - Deep reinforcement learning
<li> Chapter 20 - Why does deep learning work?
</ul> </ul>
<br> <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 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 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 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 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 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 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 14 - Unsupervised learning: Slides / Notebooks / PDF Figures / Powerpoint Figures
<li> Chapter 15 - Normalizing flows: Slides / Notebooks / PDF Figures / PowerPoint Figures <li> Chapter 15 - Variational auto-encoders: Slides / Notebooks / PDF Figures / PowerPoint Figures
<li> Chapter 16 - Generative adversarial networks: Slides / Notebooks / PDF Figures / PowerPoint Figures <li> Chapter 16 - Normalizing flows: Slides / Notebooks / PDF Figures / PowerPoint Figures
<li> Chapter 17 - Diffusion models: Slides / Notebooks / PDF Figures / PowerPoint Figures <li> Chapter 17 - Generative adversarial networks: Slides / Notebooks / PDF Figures / PowerPoint Figures
<li> Chapter 18 - Deep reinforcement learning: Slides / Notebooks / PDF Figures / PowerPoint Figures <li> Chapter 18 - Diffusion models: Slides / Notebooks / PDF Figures / PowerPoint Figures
<li> Chapter 19 - Why does deep learning work?: 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> </ul>