Intel Xeon-Phi is trying to catch up on NVIDIA

I got this article from my colleague Matt from Walmart:
http://hips.seas.harvard.edu/content/micmat-python-scientific-computing-intel-xeon-phi
it seems that Intel is trying to catch up on NVIDIA by creating tools for GPU processing.

NVIDIA has some opinion on Intel's effort which is listed here.

Hardcore data science

My friend and colleague Ben Lorica, just sent me a link to the hardcore data science track is his organizing at Strata San Jose. Super interesting topics - in addition, you are welcome to use discount code DATO20 when registering.

GraphLab's deep learning - the power of graph applied to images

GraphLab's deep learning - the power of graph applied to images

A couple of months ago we have released a deep learning toolkit for GraphLab Create. We just got a code contribution from Marian Moldovan & Enrique Otero, from Beeva.com, which utilizes GraphLab deep learning toolkit in a new and exciting ways.

Marian & Enrique created a super awesome application. Imagine you have a repository of images and you would like to understand the relation between the images. The images they used are of buildings in Barcelona, as this work was created at the hacknight of papis.io, a predictive API conference in Barcelona.

Here is the first building:

And here is the second building:

What is the architecture transition that can explain this path? Using GraphLab Create it is easy to compute!

In a nutshell, they first extracted images features using the deep learning toolkit. Then they used a nearest neighbor model to create a graph of all the similar buildings:

Next, they used the graph model to find the shortest path between two interesting buildings (number 16 and 23)

The Ipython notebook to reproduce this example is available here.

Deep learning startup raises 8M$ round A

Fresh news from my colleague Chris Debois. Richard Socher's new startup MetaMind just announced 8M$ for forming a deep learning based solution company. So far they have two functionalities: identifying objects in images and classifying text.

Readers of my blog may recall that a year ago I have already written about etcml, the project which is now the basis of this new startup.

A related company is Alchemy which provides an API for performing similar tasks. The same with Clarifai.

Other related companies are Superfish, which finds similar photos of pets, decor and clothing, as well as Cortica who identifies objects in images. The difference is that MetaMind provides tools for developers and not an end application.

By the way, in GraphLab we also provide deep learning functionality.

Google identifies content in images

I got this from my collaborator Tianqi Chen: a blog post from Google which describes their recent progress in identifying content in images and describing it using text sentences.

Some relevant papers which describes those techniques are mentioned in this ycombinator blog, I got from my collaborator Chris DeBois.

Graph Tool: Interesting graph analytics library in python

I learned about Graph Tool from Shlomo Yona from F5 Networks. Look like an interesting python package for graph analytics.

Deep learning lecture by Yann Lecun

A couple of weeks ago I attended TLV deep learning master class, organized by Prof. Lior Wolf from TAU. The highlight of the day was a tutorial lecture by Yann Lecun about this domain. Anyone who is looking to better understand this domain is advised in watching this lecture, that was just released in video:

The Unreasonable Effectiveness Of Deep Learning