— In the News —
You've likely heard about the Meltdown and Spectre vulnerabilities that affect common CPUs. The patch is said to reduce performance by up to 35% and some studies have shown performance hits that are greater 50%. Here's a look at how machine learning applications, in particular, will be affected.
— Profiles —
Boeing's CIO Ted Colbert is something of an evangelist for the power of analytics. In this interview, he talks about how data science enables new opportunities at Boeing while at the same time, presents unique challenges for a 100 year old, industry giant. Even so, what keeps him up at night is common in organizations these days. It's a lot about finding the right talent.
On the other end of the spectrum from Boeing, there are the startups. Starting with minimal resources, here's how Wish went from zero to 30 analysts and built out the data engineering and analytics infrastructure to support its product while in the midst of massive growth. This is a great read.
— Sponsored Link —
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— Tools and Techniques —
Understanding Feature Engineering
Feature engineering is an essential part of building intelligent systems. This two-part tutorial starts with the basics and walks through a wide range of approaches for common scenarios.
Nice introduction to estimating feature importance when using Random Forests. This post by Slav Ivanov succinctly describes common approaches and includes linked references for going deeper.
This curated collection of open source machine learning projects includes a variety of libraries, datasets and apps that were published in 2017. These are high quality projects that were selected on the basis of popularity, engagement and recency.
For anyone with an interest in machine learning at scale, this paper from Facebook's Research group is a must-read. This is easy to follow and offers a great overview of the hardware and software infrastructure that supports their machine learning systems on a global level.
— Blogs —
From Data Elixir Readers
Including links to blogs from Data Elixir readers has generated quite a bit of interest. If you're not sure if blogging is worth the time, definitely check out Andrew Chen's article, 10 years of professional blogging – what I’ve learned.
Here are this week's picks:
— Deep Learning —
There's a lot of hype around AI these days and this paper by Gary Marcus confronts it head on. This is well-reasoned and offers suggestions for moving forward.
— Data Viz —
In his latest post, Nathan Yau explores techniques for showing the uncertainties in your data. Includes lots of examples and discussion of pros and cons.
— Career —
Great post if you're in academia and wondering what it's like in the "real world." Covers differences between academia and industry, transferable skills, reasons to pursue a career in industry, and tips for getting hired.
— In Case You Missed It —
Be sure to catch the most popular links from last week's issue...