Modular Analytics. Top 10 AI Trends of 2018. Data Science @ Warby Parker. Deep Learning IRL. [DSR #115]

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Why Your Analytics Stack should be Modular

Frequently when we’re working with a new client at Fishtown Analytics, it can be a bit overwhelming the number of tools that are required to do sophisticated analytics: event collection, ETL, warehousing, transformation, BI… Some companies try to resolve this complexity by buying a single monolithic product that promises to do all, or a majority of, these tasks. This is a very bad idea.

This post by Roundup reader David Wallace talks about why the only good option when assembling your data stack is to go best-of-breed. If you’re spending any time thinking about your analytics tech stack today, this is a must-read.


Top 10 AI Trends for 2018

Top 10 AI Trends for 2018

Did you miss the capsule networks flurry a month ago? Haven’t been following along with probabilistic programming? This list is a solid index of terms you should be familiar with—use it to fill in your gaps.

Regular Expressions for Data Scientists

In this tutorial, learn how to use regular expressions and the pandas library to manage large data sets.

Most data scientists use regular expressions by 1) Googling “email regex”, 2) ctrl-c, 3) ctrl-v. Do yourself a favor and spend the hour or two you need to get the fundamentals.


Unsupervised Image-to-Image Translation Networks

Unsupervised Image-to-Image Translation Networks

This recent research from NVidia @ NIPS 2017 blew me away. Click through and look at these pictures in more detail. I can’t tell that the images on the right were AI-generated. Can you?


Stakeholder-Driven Data Science at Warby Parker

This 30-minute talk by Max Shron, the head of data science at Warby Parker, is awesome. In it, he focuses on answering the question “How do we make sure to build things of value?” This is the hardest question practitioners are struggling with on a day-to-day basis.

Highly recommended.


Using Deep Learning to Solve Real World Problems

There are altogether too many posts talking about deep learning but all-to-few talking about deployment in real-world scenarios. This post is a collection of feedback from several practitioners when Matthew Mayo of KDNuggets polled his Linkedin network. My favorite quote:

These projects require a radically different approach than traditional IT. They are essentially R&D projects, and it is difficult to both project the timelines and set milestones. And forget about trying to manage them internally using the same old processes.


Improving Smiling Detection with Race and Gender Diversity

From a Google talk @ NIPS 2017:

Recent progress in deep learning has been accompanied by a growing concern for whether models are fair for users, with equally good performance across different demographics. (…) We measure race and gender inclusion in the context of smiling detection, and introduce a method for improving smiling detection across demographic groups. (…) Our best-performing model defines a new state-of-the art for smiling detection, reaching 91% on the Faces of the World dataset.


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By Tristan Handy

The internet's most useful data science articles. Curated with ❤️ by Tristan Handy.

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