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Machine learning reorg
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README.md
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README.md
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* Javascript
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* [JS: The Right Way](http://jstherightway.org)
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* [Frontend developer handbook 2019](https://frontendmasters.com/guides/front-end-handbook/2019/)
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* Machine Learning
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* [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning) *(GitHub)*
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* [Best resources of everyday ML learning](https://www.quora.com/As-a-data-scientist-what-is-your-best-resource-of-everyday-learning) *(Quora)*
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* [Machine Learning Crash Course: with TensorFlow APIs](https://developers.google.com/machine-learning/crash-course/) *(Google)*
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* [Deep Learning For Coders](https://course.fast.ai)
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* [Weapons of Math Destruction](https://en.wikipedia.org/wiki/Weapons_of_Math_Destruction)
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* Big scale
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* [What is CAP Theorem?](https://www.quora.com/What-Is-CAP-Theorem-1) *(Quora)*
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* [CAP Theorem: Revisited](https://robertgreiner.com/cap-theorem-revisited/)
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@@ -218,6 +212,12 @@ A curated and **opinionated** list of resources for [Chief Technology Officers a
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* [Building a data science team](https://fastdatascience.com/building-a-data-science-team/)
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* [Managing Data Science Teams](https://www.dominodatalab.com/resources/field-guide/managing-data-science-teams/)
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* [How to Structure a Data Science Team](https://www.altexsoft.com/blog/datascience/how-to-structure-data-science-team-key-models-and-roles/)
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* Machine Learning
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* [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning) *(GitHub)*
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* [Best resources of everyday ML learning](https://www.quora.com/As-a-data-scientist-what-is-your-best-resource-of-everyday-learning) *(Quora)*
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* [Machine Learning Crash Course: with TensorFlow APIs](https://developers.google.com/machine-learning/crash-course/) *(Google)*
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* [Deep Learning For Coders](https://course.fast.ai)
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* [Weapons of Math Destruction](https://en.wikipedia.org/wiki/Weapons_of_Math_Destruction)
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## Startups
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