Data Science Weekly Newsletter

DeepSpeech: Scaling up end-to-end speech recognition
We present a state-of-the-art speech recognition system developed using end-to-end deep learning...

The NIPS Experiment
Half the papers appearing at NIPS would be rejected if the review process were rerun...

Why Neural Networks Set to Thrash Best Human Go Players for First Time
One of the last bastions of human mastery over computers is about to fall to the relentless onslaught of machine learning algorithms...

"Data Scientists at Work" - Author AMA
All the Q&A from a 2 day, wide-ranging AMA on Reddit...

Facebook offers solution to end drunken posts
Facebook is working on software that could prevent users posting unflattering photos of themselves.Combining image recognition and artificial intelligence, the system would be able to distinguish between drunk and sober pictures...

Using convolutional neural nets to detect facial keypoints tutorial
This is a hands-on tutorial on deep learning. Step by step, we'll go about building a solution for the Facial Keypoint Detection Kaggle challenge...

Reducing your R memory footprint by 7000x
R is notoriously a memory heavy language. I don't necessarily think this is a bad thing--R wasn't built to be super performant, it was built for analyzing data! ...

Exploring Seaborn and Pandas based plot types in HoloViews
In this notebook we'll look at interfacing between the composability and ability to generate complex visualizations that HoloViews provides and the great looking plots incorporated in the seaborn library...

Advanced Falconry: Seeking out the Prey with Machine Learning
The problem at hand is that we’re trying to predict gender based on measurements from the Army survey...

How to build a Hotel Review Analyzer
We combined Kimono and MonkeyLearn to create a machine learning model that learns to predict the sentiment of hotel reviews. Kimono helped us easily retrieve the training data from the web and MonkeyLearn helped us to build the sentiment analysis classifier....

Translating SQL to pandas. And back.
This tutorial will provide an introduction to both syntaxes, allowing those inexperienced with either SQL or pandas to learn a bit of both, while also bridging the gap between the two, so that practitioners of one can learn the other from their perspective. Additionally, I'll discuss the tradeoffs between each and why one might be better suited for some tasks than the other...

What do data scientists get hired to do?
One important step to properly get started in a new field is to understand what it is that the job / role actually does. Unfortunately (or fortunately), the field (data science) is so big right now that what matters differs drastically from job to job...

Data Scientist - Your Mechanic, Mountain View, CA
YourMechanic is expanding nationwide and we are looking for a Data Scientist / Data Analyst. We are looking for someone who believes that there is a lot more information in data than a cursory look can reveal. This is our first data scientist position. You will be in charge of selecting and integrating to a data solution provider (or building your own), designing and building our analysis framework and working with marketing & operations team to analyze data and inform policy decisions...

15 In-Depth Data Science Interviews - Volume 2
Given the success of our first Interview Series, we kept going! Over the past few months we have been lucky enough to conduct in-depth interviews with another 15 different Data Scientists...

PlanOut: A language for online experiments
Today, we are officially announcing the release of PlanOut 0.5, which includes a React-based PlanOut language editor, and brings the interpreter into feature-parity with the latest version of PlanOut we use internally at Facebook...

Data Science Ontology
Breakdown of the many disciplines, skills, and tools of data science...

Data Scientists at Work
JUST RELEASED: A collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession...
"In this book, you will see how some of the world's top data scientists work across a dizzyingly wide variety of industries and applications – each leveraging their own blend of domain expertise, statistics, and computer science to create tremendous value and impact..."
- Peter Norvig, Director of Research, Google

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