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Featured Blog Posts – October 2016 Archive (76)

Weekly Digest, October 10

Monday newsletter published by Data Science Central. Previous editions can be found here.  The contribution flagged with a + is our selection for the picture of the week.

Featured Resources and Technical Contributions


Added by Vincent Granville on October 8, 2016 at 9:30am — No Comments

Theano Tensors Explained in a Picture

Lately I've been doing some experiences with Theano and Deep Learning. One thing that I really thought could help is to understand the workflow of a Theano algorithm through visualization of tensors' connections. After developing the model, I printed the prediction algorithm for a deep learning Neural Net with 2 hidden layers, 2 inputs X1 and X2, and a continuous output Y. I used Graphviz and pydot to generate the graphic with this line of…


Added by Rubens Zimbres on October 7, 2016 at 3:00am — 3 Comments

Static & DYNAMICAL Machine Learning – What is the Difference?


In an earlier blog, “Need for DYNAMICAL Machine Learning: Bayesian exact recursive estimation”, I introduced the need for Dynamical ML as we now enter the “Walk” stage of “Crawl-Walk-Run” evolution of machine learning. First, I defined Static ML as follows: Given a set of inputs and outputs, find a static map between the two…


Added by PG Madhavan on October 6, 2016 at 10:04am — No Comments

What are the greatest inefficiencies data scientists face today?

This article was written by Claudia Perlich. Claudia is a Chief Scientist Distillery and Adjunct Professor at NYU. She is also a Data Scientist at Quora. 

First of, let me state what I think is NOT the the problem: the fact that data scientists spend 80% of their time with data preparation.…


Added by Emmanuelle Rieuf on October 6, 2016 at 5:30am — No Comments

Choosing the correct ML Solution for you...

               Enterprise applications trending to adopt Machine Learning as their strategic implementation and performing machine learning deep analytics across multiple problem statements is becoming a common trend. There are variety of machine learning solutions / packages / platform that exist in market. One of the main challenges that the teams initially trying to resolve is to choose the correct platform / package for their solution.

                Based on my limited…


Added by Aravindakumar Venugopalan on October 5, 2016 at 5:00pm — 1 Comment

Case Study: How a global gaming company used data analytics to beat the odds

It may be in the entertainment domain but Wargaming’s data analytics operations are as humongous as that of any Fortune 500 company. Wargaming is a leader in the free-to-play Massively Multiplayer Online (MMO) game market across all gaming platforms – PC, console and mobile.


Just to get a grasp of the enormity…


Added by Raj Dalal on October 5, 2016 at 4:30am — No Comments

Insights and Omissions from Dresner Advisory Services’ 2016 The Internet of Things and Business Intelligence Market Study




Dresner advisory services has published  a report on IoT business models. This report covers IoT, Big Data and Analytics. I have been focussing on this subject in my teaching at Oxford University and the Data Science for IoT course . So, it’s nice to see the insights. Forbes has…


Added by ajit jaokar on October 5, 2016 at 1:30am — No Comments

15 Great Blogs Posted in the last 12 Months

This is part of a new series of articles: once or twice a month, we post previous articles that were very popular when first published. These articles are at least 6 month old but no more than 12 month old. The previous digest in this series was posted here a while back. Below is our fourth edition.…


Added by Vincent Granville on October 4, 2016 at 8:51am — No Comments

Beyond Deep Learning – 3rd Generation Neural Nets

Summary:  If Deep Learning is powered by 2nd generation neural nets.  What will the 3rd generation look like?  What new capabilities does that imply and when will it get here?


By far the fastest expanding frontier of data science is AI and specifically the rapid advances in Deep Learning.  Advances in…


Added by William Vorhies on October 4, 2016 at 7:44am — No Comments

Context Levels in Data Science Solutioning in real-world

A Data science-based solution needs to address problems at multiple levels. While it addresses a business problem, computationally it is comprised of a pipeline of algorithm which, in turn, operates on relevant data presented in proper format. Thus to understand the them we need to focus at least at the

  • Business level;
  • Algorithm level; and
  • Data level.

Contrary to the popular belief, almost all non-trivial data science solutions are needed to be…


Added by Arijit Laha on October 3, 2016 at 10:30pm — No Comments

50 years of Data Science

David Donoho published a fascinating paper based on a presentation at the Tukey Centennial workshop, Princeton NJ Sept 18 2015. The paper got the attention on Hacker NewsData Science…


Added by Emmanuelle Rieuf on October 3, 2016 at 5:30pm — 1 Comment

Human Brain vs Machine Learning - A Lost Battle?

Human (or any other animal for that matter) brain computational power is limited by two basic evolution requirements : survival and procreation. Our "hardware" (physiology) and "software" (hard-coded nature psychology) only had to evolve to allow us to perform a set of basic actions - identify Friend or Foe, obtain food, find our place in the social tribe hierarchy, ultimately find a mate and multiply. Anything beyond this point, or not directly leading to this point can be…


Added by Danny Portman on October 3, 2016 at 9:30am — 5 Comments

R vs Python? No! R and Python (and something else)


Before assessing R and Python, I will start with Wolfram Mathematica. It's a powerful software, similar to MatLab. You can handle lists and matrices easily, you have all the best mathematical functions, backup of Wolfram Alpha and extremely sophisticated graphics visualizations, that allow you, for instance, to make and visualize an animated gradient descent, animate different weights for a given neural network, choose a specific…


Added by Rubens Zimbres on October 3, 2016 at 8:00am — 7 Comments

Make It So, Number One

I found some leftover hamburgers in the fridge. I decided to stack a couple of them together to form a colossal “super-burger.” At the time, I didn’t appreciate how doing so would make it almost impossible to physically fit the burger in my mouth. I squished and squeezed the burger until it was flat enough to eat. Such are the problems of physics that become…


Added by Don Philip Faithful on October 2, 2016 at 6:00am — No Comments

Weekly Digest, October 3

Monday newsletter published by Data Science Central. Previous editions can be found here.  The contribution flagged with a + is our selection for the picture of the week.


  • As businesses incorporate massive volumes of Internet of Things (IoT) and digital data, they've turned to modern architectures that include Hadoop data lakes for cost-effective storage and…

Added by Vincent Granville on October 1, 2016 at 9:30am — No Comments

Scraping with 300 req/sec in R? Yes you can!

Try the new non-blocking http API in curl 2.1:

R sitemap example, Jeroen Ooms, 2016

This code demonstrates the new multi-request features in curl 2.0. It creates an index of all files on a web server with a given prefix by recursively following hyperlinks that appear in HTML pages.

For each URL, we first perform a HTTP HEAD (via curlopt_nobody) to retrieve the…


Added by Emmanuelle Rieuf on October 1, 2016 at 8:30am — No Comments

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