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udlbook
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index.html
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@@ -3,12 +3,12 @@ by Simon J.D. Prince
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<br>
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To be published by MIT Press.
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To be published by MIT Press.
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<h2> Download draft PDF </h2>
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<h2>Key links</h2>
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<a href="https://github.com/udlbook/udlbook/releases/download/v0.2.1/UnderstandingDeepLearning_07_10_22_C.pdf">Draft PDF Chapters 2-13</a> 2022-10-07. CC-BY-NC-ND license
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<br>
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<ul>
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<ul>
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<li> <a href="https://github.com/udlbook/udlbook/releases/download/v0.2.1/UnderstandingDeepLearning_07_10_22_C.pdf">Draft PDF Chapters 2-13</a> 2022-10-07. CC-BY-NC-ND license
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<li> Chapters 1,14-19 (coming Jan 2nd, 2023)
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<li> Draft PDF Chapters 1,14-19 (coming Jan 2nd, 2023)
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<li> Jupyter notebooks (coming Spring 2023)
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<li> Report errata via <a href="https://github.com/udlbook/udlbook/issues">github</a> or contact me directly at udlbookmail@gmail.com
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<li> Report errata via <a href="https://github.com/udlbook/udlbook/issues">github</a> or contact me directly at udlbookmail@gmail.com
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<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.
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<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.
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</ul>
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</ul>
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@@ -16,18 +16,17 @@ To be published by MIT Press.
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<h2>Table of contents</h2>
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<h2>Table of contents</h2>
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<ul>
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<ul>
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<li> Chapter 1 - Introduction
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<li> Chapter 1 - Introduction
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<li> Chapter 2 - Supervised learning <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap2PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap2.pptx">PowerPoint Figures</a>
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<li> Chapter 2 - Supervised learning
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<li> Chapter 3 - Shallow neural networks <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap3PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap3.pptx">PowerPoint Figures</a>
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<li> Chapter 3 - Shallow neural networks
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<li> Chapter 4 - Deep neural networks <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap4PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap4.pptx">PowerPoint Figures</a>
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<li> Chapter 4 - Deep neural networks
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<li> Chapter 5 - Loss functions <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap5PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap5.pptx">PowerPoint Figures</a>
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<li> Chapter 5 - Loss functions
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<li> Chapter 6 - Training models <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap6PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap6.pptx">PowerPoint Figures</a>
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<li> Chapter 6 - Training models
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<li> Chapter 7 - Gradients and initialization <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>
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<li> Chapter 7 - Gradients and initialization
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<li> Chapter 8 - Measuring performance <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>
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<li> Chapter 8 - Measuring performance
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<li> Chapter 9 - Regularization <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>
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<li> Chapter 9 - Regularization
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<li> Chapter 10 - Convolutional nets <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>
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<li> Chapter 11 - Residual networks
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<li> Chapter 11 - Residual networks <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>
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<li> Chapter 12 - Transformers
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<li> Chapter 12 - Transformers <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>
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<li> Chapter 13 - Graph neural networks
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<li> Chapter 13 - Graph neural networks <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>
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<li> Chapter 14 - Variational auto-encoders
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<li> Chapter 14 - Variational auto-encoders
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<li> Chapter 15 - Normalizing flows
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<li> Chapter 15 - Normalizing flows
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<li> Chapter 16 - Generative adversarial networks
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<li> Chapter 16 - Generative adversarial networks
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@@ -48,3 +47,25 @@ Citation:
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}
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}
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</code></pre>
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</code></pre>
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<h2>Resources for instructors </h2>
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<ul>
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<li> Chapter 1 - Introduction
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<li> Chapter 2 - Supervised learning: Slides / Notebooks / <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap2PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap2.pptx">PowerPoint Figures</a>
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<li> Chapter 3 - Shallow neural networks: Slides / Notebooks / <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap3PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap3.pptx">PowerPoint Figures</a>
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<li> Chapter 4 - Deep neural networks: Slides / Notebooks / <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap4PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap4.pptx">PowerPoint Figures</a>
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<li> Chapter 5 - Loss functions: Slides / Notebooks / <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap5PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap5.pptx">PowerPoint Figures</a>
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<li> Chapter 6 - Training models: Slides / Notebooks / <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap6PDF.zip">PDF Figures</a> / <a href="https://github.com/udlbook/udlbook/raw/main/Slides/UDLChap6.pptx">PowerPoint Figures</a>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<li> Chapter 14 - Variational auto-encoders: Slides / Notebooks / PDF Figures / PowerPoint Figures
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<li> Chapter 15 - Normalizing flows: Slides / Notebooks / PDF Figures / PowerPoint Figures
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<li> Chapter 16 - Generative adversarial networks: Slides / Notebooks / PDF Figures / PowerPoint Figures
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<li> Chapter 17 - Diffusion models: Slides / Notebooks / PDF Figures / PowerPoint Figures
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<li> Chapter 18 - Deep reinforcement learning: Slides / Notebooks / PDF Figures / PowerPoint Figures
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<li> Chapter 19 - Why does deep learning work?: Slides / Notebooks / PDF Figures / PowerPoint Figures
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</ul>
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