Wednesday, 7 September 2016

Blog migration announcement

My technical blog has moved to since early 2016.
While the Blogger template has served me well so far, I was keen on using a toolkit similar to Github publishing. The Cryogen project was the ideal choice, as it is built in Clojure and allowed me to do interesting enhancements without having to learn a new language.

So that's that. Hope to see you there! :)

Monday, 18 April 2016

Html template generation in Clojure - with Enlive

Enlive Tutorial

While working with web applications in Clojure, I had the opportunity to work with the excellent HTML templating engine Enlive. I'm sharing a tutorial (I had written) on Enlive, which is at this Github worksheet

p.s. To run the worksheet, click on the "view this worksheet on Github" link on the top right corner. Alternatively, you can clone the Github repo, run lein gorilla at the terminal, and open the worksheet located at src/enliven.cljw.

Tuesday, 12 April 2016

Presentation on Probabilistic Graphical Models

Its been about a year and a half since my book Building Graphical Models in Python was published. Since the book is fairly technical and involves programming, it is not the easiest of introductions to graphical models, especially to somebody with little knowledge of Machine Learning.

To introduce the topic and whet people's knowledge about Graphical Models, I have given the following presentation a couple of times, and it was well received as an introduction to the lay person. Here it is.

Topic Modeling on Customer Experience data


There exists a vast trove of Customer Experience data in the form of product reviews, forum posts, customer service/customer satisfaction surveys and suchlike. This data is often in unstructured form. Companies that own this data would like to summarize these (often vast) data-sets.

One of the most common methods of text mining (for lack of a better word), is Topic Modeling. Given a large corpus of text, a topic model can assign a probabilistic score for each document-topic pair.

At BAICONF'15, I presented a paper and presentation on the effectiveness of using Topic Modeling for summarizing Customer Experience data. This paper was the result of our experiences (working with Extrack at Bridgei2i) of applying multiple methods such as Unsupervised Topic Models, Semi-Supervised Topic Models and others, on multiple types of Customer Experience data. The paper, as well as the presentation, are presented below. (Note, due to scheduling issues, the paper/presentation is not listed in the BAICONF schedule).

Here's the link to the paper along with the accompanying presentation.