— Insight —
New research released by the MIT Sloan Management Review shows how organizations that are pioneering the use of AI are overtaking their more conservative counterparts. This report highlights the major findings from a survey of over 3000 business leaders and shows how the winners are prioritizing revenue-generating applications over cost-saving ones.
— Sponsored Link —
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— Tools and Techniques —
Here's a useful guide for identifying and fixing reproducibility problems in machine learning projects.
This overview of the rapidly evolving deep learning landscape considers a variety of sources to gauge the popularity of the most important deep learning frameworks. Considerations include usage, google search activity, publications, Github activity, and job listings.
Using computer vision to diagnose diseases is a commonly cited use-case for machine learning in healthcare but it's not the only use. This article explores how machine learning could have broad impacts in medicine by applying NLP techniques to Electronic Healthcare Records. This is easy to follow and is a good introduction to ways that NLP can be applied to industries where text data is critical.
— Resources —
In her latest Ask-A-Data-Scientist advice column, Rachel Thomas offers an extensive list of resources for AI ethics. Includes links to syllabi, experts to follow, institutes, fellowships, talks, articles, etc.
— Data Viz —
Smart use of Facebook data by The New York Times.
Great "cheat sheet" for Matplotlib that includes lots of examples in a Jupyter notebook.
— Career —
This deep dive into tens of thousands of resumes explores career paths in the data science community, including fields of study, levels of education, and prior jobs. There are also insights here into the similarities and differences between various roles such as data scientists, analysts, software engineers, and machine learning engineers.