July 2014

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Carl sent me a YouTube video by Dr  Gunnar Carlsson on the application of Topology to Data Mining (Topological Data Analysis).

Dr. Carlsson created a short 5 minute introduction, and a longer video of one of his lectures.

For more information, check out “Topology based data analysis identifies a subgroup of breast cancers with a unique mutational profile and
excellent survival” by Nicolaua, Levineb, and Carlsson.

Also, Bansal and Choudhary put together a nice set of slides on the subject with applications to clustering and visualization.



Christopher Olah wrote an incredibly insightful post on Deep Neural Nets (DNNs) titled “Deep Learning, NLP, and Representations“.  In his post, Chris looks at Deep Learning from a Natural Language Processing (NLP) point of view.  He discusses how many different deep neural nets designed for different NLP tasks learn the same things.   According to Chris and the many papers he cites, these DNNs will automatically learn to intelligently embed words into a vector space.  Words with related meanings will often be clustered together.  More surprisingly, analogies such as “France is to Paris as Italy is to Rome” or “Einstein is to scientist as Picasso is to Painter” are also learned by many DNNs when applied to NLP tasks.  Chris reproduced the chart of analogies below from “Efficient Estimation of Word Representations in Vector Space” by Mikolov, Chen, Corrado, and Dean (2013).

Relationship pairs in a word embedding. From Mikolov et al. (2013).

Additionally, the post details the implementation of recurrent deep neural nets for NLP.  Numerous papers are cited, but the writing is non-technical enough that anyone can gain insights into how DNNs work by reading Chris’s post.

So why don’t you just read it like NOW  — CLICK HERE.   :)