Free book PDF — official first edition of “Machine Learning for High-Risk Applications.”

https://pages.dataiku.com/oreilly-responsible-ai — Thanks Dataiku!

What’s this book about? See the attached image for 250 extracted topics from the text displayed as an undirected graph. The nodes are sparse NMF-extracted topics, where the Pearson correlation between the topics forms the edge weights. Colors are assigned using a community detection algorithm from Gephi.

A high-resolution image is available here: https://drive.google.com/file/d/1SOZBxCYxea2UvzAqTzXorKiJUtgQPARZ/view?usp=drivesdk . (It’s ok to use simpler, interpretable language models sometimes ;)

Since the book came out in late April, I’ve been busy with several announcement activities, and some interesting events are coming up soon. If you’d like to learn more about the book, #machinelearning or #responsibleai, check out:

• Two recent podcasts:
• — The Data Exchange Podcast with Ben Lorica 罗瑞卡 and Agus Sudjianto: https://thedataexchange.media/machine-learning-for-high-risk-applications/
• — The MLSecOps Community Podcast with Daryan D. and Charlie McCarthy: https://mlsecops.com/podcast/patrick-hall-5/10
• - An upcoming talk with Dataiku and Triveni Gandhi, Ph.D: https://www.brighttalk.com/webcast/17108/584137?utm_source=Dataiku&utm_medium=brighttalk&utm_campaign=584137
• - An upcoming QuantUniversity summer lecture with Sri Krishnamurthy, CFA, CAP
• As always thanks to my heroic co-authors James Curtis and Parul Pandey!
• Please let us know your feedback on the book:
• - Errata: https://www.oreilly.com/catalog/errata.csp?isbn=0636920509684
• - GitHub issues: https://github.com/ml-for-high-risk-apps-book/Machine-Learning-for-High-Risk-Applications-Book/issues

免费书籍 PDF — “高风险应用程序的机器学习”的官方第一版。

https://pages.dataiku.com/oreilly-responsible-ai — 感谢 Dataiku！

这本书讲的是什么？从显示为无向图的文本中提取 250 个主题，请参见附件图像。节点是稀疏 NMF 提取的主题，其中主题之间的 Pearson 相关性形成边权重。使用 Gephi 的社区检测算法分配颜色。

此处提供高分辨率图像：https://drive.google.com/file/d/1SOZBxCYxea2UvzAqTzXorKiJUtgQPARZ/view?usp=drivesdk。(有时使用更简单、可解释的语言模型是可以的；)

自从这本书在 4 月下旬出版以来，我一直在忙于几个公告活动，一些有趣的事件即将发生。如果您想了解有关本书#machinelearning 或#responsibleai 的更多信息，请查看：

• 最近的两个播客：
• — Ben Lorica 罗瑞卡和 Agus Sudjianto 的数据交换播客：https://thedataexchange.media/machine-learning-for-high-risk-applications/
• — Daryan D. 和 Charlie McCarthy 的 MLSecOps 社区播客：https://mlsecops.com/podcast/patrick-hall-5/10
• - 即将与 Dataiku 和 Triveni Gandhi 博士进行的谈话：https://www.brighttalk.com/webcast/17108/584137?utm_source=Dataiku&utm_medium=brighttalk&utm_campaign=584137
• - 即将到来的 QuantUniversity 夏季讲座，主讲人是 CFA、CAP 的 Sri Krishnamurthy
• 一如既往地感谢我英勇的合著者 James Curtis 和 Parul Pandey！
• 请让我们知道您对本书的反馈：
• -勘误表：https://www.oreilly.com/catalog/errata.csp?isbn=0636920509684
• - GitHub 问题：https://github.com/ml-for-high-risk-apps-book/Machine-Learning-for-High-Risk-Applications-Book/issues

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