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    <title>1. Introduction - Generative AI Design Patterns [Book]</title>
    <dc:date>2025-06-28T04:13:48+00:00</dc:date>
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    <dc:creator>amy</dc:creator><dc:subject>machine_learning genAI design_patterns books</dc:subject>
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    <title>Generative AI Design Patterns</title>
    <dc:date>2025-06-12T09:36:39+00:00</dc:date>
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    <dc:creator>amy</dc:creator><description><![CDATA[Generative AI enables powerful new capabilities, but they come with some serious limitations that you'll have to tackle to ship a reliable application or agent. Luckily, experts in the field …  - Selection from Generative AI Design Patterns [Book]

Authors Valliappa Lakshmanan and Hannes Hapke codify advances in research and real-world experience into advice that you can readily incorporate into your projects...]]></description>
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    <title>An ex-Google AI ethicist and a UW professor want you to know AI isn't what you think it is</title>
    <dc:date>2025-05-12T16:25:00+00:00</dc:date>
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    <dc:creator>amy</dc:creator><description><![CDATA[BI spoke with authors Emily Bender and Alex Hanna about their new book, "The AI Con: How to Fight Big Tech's Hype and Create the Future We Want."]]></description>
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    <title>Books Written Without AI Can Now Receive New 'Human Authored' Certification</title>
    <dc:date>2025-02-04T13:35:26+00:00</dc:date>
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    <dc:creator>amy</dc:creator><description><![CDATA[As AI-generated media enters the mainstream, there is hope that consumers will put a premium on handmade works.]]></description>
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    <title>AI Snake Oil</title>
    <dc:date>2024-12-20T14:54:17+00:00</dc:date>
    <link>https://press.princeton.edu/books/hardcover/9780691249131/ai-snake-oil</link>
    <dc:creator>amy</dc:creator><description><![CDATA[From two of TIME’s 100 Most Influential People in AI, what you need to know about AI—and how to defend yourself against bogus AI claims and products]]></description>
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    <dc:date>2023-07-23T03:58:17+00:00</dc:date>
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    <dc:creator>amy</dc:creator><description><![CDATA[Kevin Murphy]]></description>
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    <title>[2106.10165] The Principles of Deep Learning Theory</title>
    <dc:date>2022-04-17T02:05:59+00:00</dc:date>
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    <dc:creator>amy</dc:creator><description><![CDATA[The Principles of Deep Learning Theory

Relatively accessible and self-contained textbook (concepts explained from first principles) on deep learning theory.

Gives a theoretical perspective on topics from network initialization to representation learning.

This book develops an effective theory approach to understanding deep neural networks of practical relevance. Beginning from a first-principles component-level picture of networks, we explain how to determine an accurate description of the output of trained networks by solving layer-to-layer iteration equations and nonlinear learning dynamics. A main result is that the predictions of networks are described by nearly-Gaussian distributions, with the depth-to-width aspect ratio of the network controlling the deviations from the infinite-width Gaussian description. We explain how these effectively-deep networks learn nontrivial representations from training and more broadly analyze the mechanism of representation learning for nonlinear models. From a nearly-kernel-methods perspective, we find that the dependence of such models' predictions on the underlying learning algorithm can be expressed in a simple and universal way. To obtain these results, we develop the notion of representation group flow (RG flow) to characterize the propagation of signals through the network. By tuning networks to criticality, we give a practical solution to the exploding and vanishing gradient problem. We further explain how RG flow leads to near-universal behavior and lets us categorize networks built from different activation functions into universality classes. Altogether, we show that the depth-to-width ratio governs the effective model complexity of the ensemble of trained networks. By using information-theoretic techniques, we estimate the optimal aspect ratio at which we expect the network to be practically most useful and show how residual connections can be used to push this scale to arbitrary depths. With these tools, we can learn in detail about the inductive bias of architectures, hyperparameters, and optimizers.
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    <title>Hands_on_Machine_Learning_with_Scikit_Le.html</title>
    <dc:date>2018-05-01T14:39:00+00:00</dc:date>
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    <title>Neural networks and deep learning</title>
    <dc:date>2016-10-05T14:04:45+00:00</dc:date>
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    <dc:creator>amy</dc:creator><description><![CDATA[Michael Nielsen]]></description>
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    <title>Machine Learning Yearning</title>
    <dc:date>2016-06-21T14:27:19+00:00</dc:date>
    <link>http://www.mlyearning.org/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[<blockquote>
Get a free draft copy of my book on how to structure Machine Learning projects: http://mlyearning.org I’d started this before but got distracted building Deep Learning Specialization; I’m now rebooting this. Sign up to get free chapters as they’re released!
</blockquote>]]></description>
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    <title>Deep Learning</title>
    <dc:date>2016-04-07T16:25:10+00:00</dc:date>
    <link>http://www.deeplearningbook.org/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[<blockquote>
The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. The print version will be available for sale soon.


</blockquote>]]></description>
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    <title>Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition.</title>
    <dc:date>2009-10-16T17:39:45+00:00</dc:date>
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