Data Science Weekly Newsletter

Issue

August 28, 2014

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Editor's Picks

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The Plan to Build a Massive Online Brain for All the World’s Robots
Today, backed by funding from the National Science Foundation, the Office of Naval Research, Google, Microsoft, and Qualcomm, Saxena and his team unveiled what they call RoboBrain, a kind of online service packed with information and artificial intelligence software that any robot could tap into...

The Data Scientist on a Quest to Turn Computers Into Doctors
Some of the world’s most brilliant minds are working as data scientists at places like Google, Facebook, and Twitter—analyzing the enormous troves of online information generated by these tech giants—and for hacker and entrepreneur Jeremy Howard, that’s a bit depressing...

Amazon’s new ad network has a secret weapon against Google AdWords: shopping data
The secret to delivering relevant ads is data. Demographic, psychographic, personal data. Google has data on what we search for, Facebook has data on what we like and share, but Amazon’s data may be the best of all for targeting ads. It has data on what we browse and buy...

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A Day in the Life of a Functional Data Scientist
Richard Minerich explains how ideas and tools from functional programming can save time, prevent subtle mistakes in data science, and how he incorporates them into his everyday workflow...

The Role of Chief Data Officer in the 21st Century
Fifteen years after enterprise resource planning and over two decades into data warehousing, many business executives are still frustrated over their inability to trust their company's data One big reason for this continuing data chaos is that companies do not manage their data as a business asset, and there is no one watching the store. In this Executive Report, we look at the role of chief data officer and why this role is so important...

Machine learning teaches me how to write better AI
Writing AI to play games is a special kind of crack for me. Seeing my AI face off against other people - or better yet, AIs written by other people! It’s so… I don’t know how to describe it. I cannot resist its siren call...

Practical Lessons from Predicting Clicks on Ads at Facebook
With over 750 million daily active users and over 1 million active advertisers, predicting clicks on Facebook ads is a challenging machine learning task. In this paper we introduce a model which combines decision trees with logistic regression, outperforming either of these methods on its own by over 3%, an improvement with significant impact to the overall system performance...

Hacking Academia: Data Science and the University
A reflection on our SciFoo breakout session, where we discussed issues of data science within academia...

Automatic Storytelling: Or, How to Build Your Very Own Data Scientist
Unfortunately, the process of analyzing data and compiling interesting results can be very time consuming. Even so, after telling these stories many times, some patterns emerge in the data analysis and communication of the findings. This led the data science team at Chartbeat to ask the question: Can we build an automated data scientist that can seek out interesting stories within our data?...

How the Napa Earthquake Affected Bay Area Sleepers
The South Napa Earthquake was the strongest to hit Northern California in 25 years. Our data science team wanted to quantify its effect on sleep by looking at the data recorded by Jawbone UP wearers in the Bay Area who track their sleep patterns...

Mood for Music: Emotion Recognition on Acoustic Features
Our emotional response to a music fragment depends on a large set of external factors, such as gender, age, culture, and context. However, these external variables set aside, humans consistently categorise songs as being happy, sad, enthusiastic or relaxed. We’ve developed an algorithm that knows how a song will be emotionally perceived. ...

On Machine Learning
My aim is not to focus on the algorithms, methods or classifiers but rather to offer a broader picture on how to approach a machine learning problem, and in the meantime give some advice...

3x faster convolutions in Theano
Convolutional neural networks (convnets) are all the rage right now. Training a convnet on any reasonably sized dataset is very computationally intensive, so GPU acceleration is indispensible. In this post I’ll show how you can use the blazing fast convolution implementation from Alex Krizhevsky’s cuda-convnet in Theano....

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Data Engineer - SumAll - New York City
SumAll is a connected data tool that helps thousands make better decisions using data. As a Data Engineer you will be part of the Data Products group. You will be working closely with other Data Scientists, Integration Engineers, Product Engineers, and Designers to help develop our flagship analytics application. Your main responsibility will be to help develop software and systems to mine data for trends, patterns, and insights that can be delivered to our users...

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Artificial Dataset Generation for Machine Learning w/ Python, Numpy, Theano
In this quick post I just wanted to share some Python code which can be used to benchmark, test, and develop Machine Learning algorithms with any size of data...

An Introduction to Deep Learning: From Perceptrons to Deep Networks
In this article, I’ll introduce you to the key concepts and algorithms behind Deep Learning, beginning with the simplest unit of composition and building from there...

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Learn R in a Day
Clear and efficient way to get up and running in R...
"I was delighted with this little book...it got me functional with R, able to enter, manipulate, and plot data usefully in less than 8 hours of work..."...

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