Data Science Weekly Newsletter

Issue

September 3, 2015

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A Message From This Week's Sponsor

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Selling out with over 5,500 attendees last year, the biggest data conference is returning to New York September 29-October 1. Strata + Hadoop World gears up for its 5th year with all-new program tracks: Data-driven Business, Data Innovations, Data Science & Analytics, IoT & Real-time, Organizational Changes, Spark & Beyond, and more. Don't miss what Wired magazine calls “the lollapalooza of big data conferences.”
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How Etsy Uses Thermodynamics to Help You Search for “Geeky”
I wrote about the ways my team at Etsy uses math to help you search when you have no idea what you're searching for...

Maxima Core AI Atari Demo
AI startup apparently outperforming Deep Q-Learning on Atari games...

Predicting future returns of trading algorithms: Bayesian cone
When evaluating trading algorithms we generally have access to backtest results over a couple of years and a limited amount of paper or real money traded data. The biggest issue with evaluating a strategy based on the backtest is that it might be overfit to look good only on past data but will fail on unseen data. In this blog, we will take a stab at addressing this problem using Bayesian estimation and prediction of possible future returns we expect to see based on the backtest results...

Deep Neural Network Learns Van Gogh's Art
Artificial neural networks were inspired by the human brain and simulate how neurons behave when they are shown a sensory input (e.g., images, sounds, etc). They are known to be excellent tools for image recognition... This time they have been shown to be apt at reproducing the artistic style of many famous painters, such as Vincent Van Gogh and Pablo Picasso among many others. All the user needs to do is provide an input photograph and a target image from which the artistic style will be learned...

Why Deep Learning Will Lead To New, Troublesome Art
The increasing sophistication of Deep Learning artificial intelligence techniques are going to lead to a new type of generative art. That’s going to be exciting for our culture, but may draw the ire of rights holders...

Calculus on Computational Graphs: Backpropagation
Backpropagation is the key algorithm that makes training deep models computationally tractable. For modern neural networks, it can make training with gradient descent as much as ten million times faster, relative to a naive implementation. That’s the difference between a model taking a week to train and taking 200,000 years...

Notes on Semi-Supervised Learning with Ladder Network
This paper describes a learning algorithm for deep neural networks that can be understood as an extension of stacked denoising autoencoders. In short, instead of reconstructing one layer at a time and greedily stacking, a unique unsupervised objective involving the reconstruction of all layers is optimized jointly by all parameters (with the relative importance of each layer cost controlled by hyper-parameters)...

First Steps in Data Science: Author-Aware Sentiment Analysis
People often ask me what’s the best way of becoming a data scientist. The way I got there was by first becoming a software engineer and then doing a PhD in what was essentially data science (before it became such a popular term). This post describes my first steps in the field with the goal of helping others who are interested in making the transition from pure software engineering to data science...

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Data Scientist - Square - San Francisco, CA The Analytics Team at Square leads data-science projects that derive value from our unique, rich, and rapidly growing data. We partner with product, marketing, and operations teams to drive actionable insights into customer behavior, operational efficiency, and risk. We’re a passionate team of hackers, statisticians, and optimizers who are resourceful in distilling questions, wrangling data, and driving decisions. You, as a Data Scientist will partner closely with both internal and customer-facing teams to bring the voice of our customers to life through data....

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Welcome to the unofficial Google data science blog
Despite Google’s technical achievements with big data, it may come as a surprise that there is no official Google blog for data science. True, Google Research puts out many academic papers and has a blog describing matters of interest to researchers. But what has been missing to date is a conversation about the nuts-and-bolts, the day-to-day of large scale analytical systems Google builds to serve its users. We’d like to change that...

Bayesian Correlation with PyMC
In this notebook, I show how to determine a correlation coefficient within the Bayesian framework both in a simply and a robust way. The correlation can be seen as a direct alternative to the traditional Pearson correlation coefficient...

proof 0.1.0 (alpha)
proof is a Python library for creating optimized, repeatable and self-documenting data analysis pipelines...

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Naked Statistics: Stripping the Dread from the Data Interesting take on the importance of statistics...
"While a great measure of the book’s appeal comes from Mr. Wheelan’s fluent style—a natural comedian, he is truly the Dave Barry of the coin toss set—the rest comes from his multiple real world examples illustrating exactly why even the most reluctant mathophobe is well advised to achieve a personal understanding of the statistical underpinnings of life" - New York Times... For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page...

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