Apologies if anyone got “Page Not Found” errors from clicking links in last week’s issue! It’s been sorted out.
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My favorite post of the week. The author deals with a topic I see people struggling with all the time: how to learn new skills when you already have a full-time job. It has a bunch of great lines that I absolutely agree with:
The best way to learn a new skill is using it to solve problems.
It doesn’t take much knowledge to achieve useful results
My most-often given advice to folks looking to transition into a career in data is to find opportunities to deploy new skills at their current jobs. You can almost always find these opportunities if you look.
This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math where needed.
Didn’t take linear algebra (or forgotten most of what you learned)? No problem, the authors have written this just for you. You’ll feel much more confident deploying deep learning models when you can follow the math all the way down.
This is a very basic concept, but one that I find people mix up frequently. If you’ve ever had a timeseries of monthly growth rates and then to find the average monthly growth rate you just use an arithmetic mean, that was the wrong thing to do. Read this post to learn why :)
Want to calculate a geometric mean in SQL? Easy.
Andrew Ng just announced his new project, AI Fund:
In the early days of electricity, much of the innovation centered around slightly different improvements in lighting. While this was an important foundation, the really transformative applications, in which electric power spurred massive redesigns in multiple industries, took longer to be grasped. AI is the new electricity, and is at a similar inflection point.
The launch received quite a bit of attention this week. I’m sure we’ll be hearing much more from them in the coming months.
As was the case with the mobile revolution, and the web before that, machine learning will cause us to rethink, restructure, and reconsider what’s possible in virtually every experience we build. In the Google UX community, we’ve started an effort called “human-centered machine learning” to help focus and guide that conversation.
We’ve certainly seen the role of the data engineer, analyst, and scientist evolve rapidly over the course of the past 5 years. Moving forwards, it’s very possible that the biggest role to change will be that of the product manager. Google is thinking about this topic a lot, and this writeup provides a lot of applied examples.
Food for thought.
The Academy Award nominations announced Tuesday included the box-office hits “Get Out,” “Lady Bird” and “The Post” among the contenders for best picture. A number of the nominees, however, haven’t been released widely to the public and are showing in less than 15% of theaters in North America.
It’s part of some studios’ strategy to release their films slowly, building on word of mouth and, indeed, award nominations. Unlike a “wide release,” in which a new film opens in more than 1,500 theaters on the same weekend, a “platform release” of a film means it will typically open in fewer than 50 theaters to start.
This is an excellent piece of data journalism! The initial viz is great, but what impressed me away was how they continued to peel back the onion. Also, it seems like I have a lot of movies to catch up on!
You can now get access to all historical tweets (back to 2006!) via the search API. Twitter is one of the largest text corpora ever, and this full-history text search will certainly open up new avenues for research.
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The internet's most useful data science articles. Curated with ❤️ by Tristan Handy.
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