Data Science Weekly Newsletter

Issue

October 9, 2014

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Editor's Picks

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Discover the Methodology and Mindset of a Kaggle Master:
An Interview with Diogo Ferreira
What does it take to do well in competitive machine learning? To really dig into this question, you need to dig into the people that do well...

Deep Learning RNNaissance
My talk summarizes our work on DL since 1991. Our recurrent NNs (RNNs) were the first to win official international competitions in pattern recognition and machine learning; our team has won more such contests than any other research group or company. In particular, our RNNs represent the state of the art in connected handwriting recognition, and aspects of speech recognition. We also built the first artificial RNN-based agent that learns from scratch complex control based on high-dimensional vision....

Deep Learning for Detecting Robotic Grasps
Learning-based approaches in previous works have been succeesfully used for grasping novel objects, but required manual design of features for image and depth data. We use deep learning, which allow us to learn the basic features used by our algorithm directly from RGB-D data...

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Intriguing properties of neural networks
Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties...

Machine learning is the new algorithms
My claim today---and I'm speaking really as an NLP person, which is how I self-identify---is that machine learning is the new core. Everything that algorithms was to computer science 15 years ago, machine learning is today. That's not to say it won't move in another 10 years, but that's how I see it...

You are not your browser history
What $50 taught me about my web ad identity– and how I’m fighting back...

Data Science is mainly a Human Science
Data Science is mainly a Human Science: in latest KDnuggets Poll, 62% of data scientists worked only/mostly on human data...

Taxi Techblog 2: Leaflet, D3, and other Frontend Fun
This is part 2 of my techblog about NYC Taxis: A Day in the Life. In part 1, I showed how I queried the necessary data, manipulated it a bit, and built a simple node server to supply geoJSON to the client. Now I’ll discuss how I turned that geoJSON into an animated map and associated charts...

Do Deep Nets Really Need to be Deep?
Currently deep neural networks are the state of the art on problems such as speech recognition and computer vision. In this extended abstract we show that shallow feed-forward networks can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models...

Misleading With Statistics
How journalists make arguments with distorted data...

Situational thinking in football - How can data help?
What is the current state of data-driven football research? Where can we improve? I've written before about smugness and overconfidence in sports analytics. It's a real problem. But we also know quite a bit. As an exercise, I thought I'd break down open areas of research into categories and identify where we have room to grow (and I'm sure a lot of this could apply to other sports as well)...

The Browsemaps: Collaborative Filtering at LinkedIn
Many web properties make extensive use of item-based collaborative filtering, which showcases relationships between pairs of items based on the wisdom of the crowd. This paper presents LinkedIn's horizontal collaborative filtering infrastructure, known as browsemaps...

A flurry of copycats on PubMed
It started with a search for trends on PubMed. I am not sure what I expected to find, but it was nothing like the “CISCOM meta-analyses”. Here is the story of how my colleague Lucas Carey (from Universitat Pompeu Fabra) and myself discovered a collection of disturbingly similar scientific papers, and how we got to the bottom of it...

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Data Scientist, WalmartLabs - Brisbane, CA
Do you like big data? Like really big data? Like multi-terabyte data sets with billions of rows? Do you like the idea of pulling, pushing, slicing and dicing this data in real-time using using Hadoop, Hbase, Hive and more? Now let's add some intelligence to the mix using machine learning, data mining and predictive analytics and shazam - you have the underpinnings of @WalmartLabs...

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Wabbit Wappa 0.2.0
Wabbit Wappa is a full-featured Python wrapper for the lightning fast Vowpal Wabbit ("VW") machine learning utility. Wabbit Wappa makes it easier to use VW's powerful features while abstracting away its idiosyncratic syntax and interface...

Testing with NumPy and Pandas
Testing Python results is often as straightforward as assert result == expected, especially with builtin types. But that doesn’t work with NumPy or Pandas data structures...

Random Forests Tutorial
Random forests, aka decision forests, and ensemble methods...

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Automate This: How Algorithms Took Over Our Markets, Jobs & the World
The story of how algorithms took over and shows why the “bot revolution” is about to spill into every aspect of our lives...
"Well written with tons of recent and relevant examples. Scary how much control of day to day information is being collected and manipulated by algorithms..."...

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