Pete Warden

A former Apple engineer, Pete Warden is the founder of OpenHeatMap, and writes on large-scale data processing and visualization.

Why deep belief matters so much

We’re entering a new world where computers can see.

If you’re a programmer who reads the Internet, you’ll have heard of deep belief networks. Google loves them, Facebook just hired one of the pioneers to lead a new group, and they win Kaggle competitions. I’ve been using deep belief networks extensively for image recognition at Jetpac across hundreds of millions of Instagram photos, and the…
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How to analyze 100 million images for $624

There's a lot of new ground to be explored in large-scale image processing.

Jetpac is building a modern version of Yelp, using big data rather than user reviews. People are taking more than a billion photos every single day, and many of these are shared publicly on social networks. We analyze these pictures to discover what they can tell us about bars, restaurants, hotels, and other venues around the world —…
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How to create a visualization

How to create a visualization

Pete Warden walks through the steps behind his latest Facebook visualization.

Creating a visualization requires more than just data and imagery. Pete Warden outlines the process and actions that drove his new Facebook visualization project.

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3 ideas you should steal from HubSpot

3 ideas you should steal from HubSpot

HubSpot has found the sweet spot between data, education and customer loyalty.

HubSpot's location (near Boston) and its target market (small businesses) may keep it under the radar of Silicon Valley, but the company's approach to data products and customer empowerment are worthy of attention.

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Lessons of the Victorian data revolution

Lessons of the Victorian data revolution

Transaction costs, crowdsourcing, and the persuasiveness of data were all in play long ago.

Examples from the Victorian era show that if we're going to improve the world with data, it's absolutely essential we stay grounded in reality.

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Why you can't really anonymize your data

It's time to accept and work within the limits of data anonymization.

Because we now have so much data at our disposal, any dataset with a decent amount of information can be matched against identifiable public records. To keep datasets available, we must acknowledge that foolproof anonymization is an illusion.

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Why the term "data science" is flawed but useful

Counterpoints to four common data science criticisms.

While formal boundaries and professional criteria for "data science" remain undefined, here's why we should keep using the term.

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The iPhone tracking story, one week later

The iPhone tracking story, one week later

Apple issues a statement on location and says iOS fixes are coming soon.

Apple announces fixes and sheds more light on location data. Plus, a look at some of the reporting and potential applications that have popped up.

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Additional iPhone tracking research

Researchers and reporters are exploring many of the issues related to mobile location data.

The iPhone tracking story led to a host of related investigations. Here's a look at some of the latest developments.

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iPhone tracking: The day after

iPhone tracking: The day after

Analysis and criticism came in the wake of our iPhone tracking story.

The iPhone tracking story published here a few days ago struck an unexpected nerve. Here's a selection of the most interesting immediate reactions.

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