77 lines
5.7 KiB
HTML
77 lines
5.7 KiB
HTML
<h1>Understanding Deep Learning</h1>
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by Simon J.D. Prince
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<br>
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To be published by MIT Press.
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<h2> Download draft PDF </h2>
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<a href="https://github.com/udlbook/udlbook/releases/download/v0.6.1/UnderstandingDeepLearning_02_02_23_C.pdf">Draft PDF Chapters 1-19</a><br> 2023-02-02. CC-BY-NC-ND license
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<br>
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<img src="https://img.shields.io/github/downloads/udlbook/udlbook/total" alt="download stats shield">
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<br>
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<ul>
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<li> Chapter 20 and appendices coming soon
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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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</ul>
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<h2>Table of contents</h2>
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<ul>
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<li> Chapter 1 - Introduction
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<li> Chapter 2 - Supervised learning
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<li> Chapter 3 - Shallow neural networks
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<li> Chapter 4 - Deep neural networks
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<li> Chapter 5 - Loss functions
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<li> Chapter 6 - Training models
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<li> Chapter 7 - Gradients and initialization
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<li> Chapter 8 - Measuring performance
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<li> Chapter 9 - Regularization
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<li> Chapter 10 - Convolutional networks
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<li> Chapter 11 - Residual networks
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<li> Chapter 12 - Transformers
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<li> Chapter 13 - Graph neural networks
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<li> Chapter 14 - Unsupervised learning
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<li> Chapter 15 - Generative adversarial networks
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<li> Chapter 16 - Normalizing flows
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<li> Chapter 17 - Variational auto-encoders
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<li> Chapter 18 - Diffusion models
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<li> Chapter 19 - Deep reinforcement learning
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<li> Chapter 20 - Why does deep learning work?
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</ul>
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<br>
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Citation:
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<pre><code>
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@book{prince2023understanding,
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author = "Simon J.D. Prince",
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title = "Understanding Deep Learning",
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publisher = "MIT Press",
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year = 2023,
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url = "https://udlbook.github.io/udlbook/"
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}
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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 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>
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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 - Unsupervised learning: Slides / Notebooks / PDF Figures / Powerpoint Figures
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<li> Chapter 15 - Generative adversarial networks: Slides / Notebooks / PDF Figures / PowerPoint Figures
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<li> Chapter 16 - Normalizing flows: Slides / Notebooks / PDF Figures / PowerPoint Figures
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<li> Chapter 17 - Variational auto-encoders: Slides / Notebooks / PDF Figures / PowerPoint Figures
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<li> Chapter 18 - Diffusion models: Slides / Notebooks / PDF Figures / PowerPoint Figures
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<li> Chapter 19 - Deep reinforcement learning: Slides / Notebooks / PDF Figures / PowerPoint Figures
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<li> Chapter 20 - Why does deep learning work?: Slides / Notebooks / PDF Figures / PowerPoint Figures
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</ul>
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