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I bumped into an elderly co-passenger in train the other day who was struggling to manage his contacts on phone. Sensing that I am a technology guy (due to my laptop, I guess) he asked me for help. Now for any of us who have grown in the word of technology, this is certainly not the most difficult problem to solve. Sure enough, I helped him out and was satisfied to see a smile on his face.

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One of the better introductions to Machine Learning I have come across till now. It is at a very high level, I must admit, but the non-mathematical intuition behind the most basic and fundamental algorithms of Machine Learning has been very nicely covered with appropriate examples. Good job, Luis Serrano. Do check out!

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Gave a talk today in the Machine Learning reading group of CSE@IITB. What could have been a better choice than something hot off the press from CVPR 2016? YOLO’s claim to fame is that it is the most accurate real-time object detector and is also the fastest object detector in literature today. Here is the presentation I made. Enjoy!

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Need to give a talk or conduct a lecture? Wondering what is one of the more effective ways of explaining a concept to somebody who has absolutely no idea of what it is about? Try A.D.E.P.T. This idea from Kalid Azad of betterexplained.com seems quite interesting.

  1. A - Analogies
  2. D - Diagrams
  3. E - Examples
  4. P - Plain Language
  5. T - Technical Definition

[Image courtesy: alistapart.com]

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I am currently in Hyderabad attending a 6-day summer school on deep learning for computer vision organized by Center for Visual Information Technology, IIIT Hyderabad. Couldn’t have asked for a better start to my PhD - with renowned speakers presenting the state-of-the-art in this space and with lots of hand on lab sessions. If you are interested in deep learning in general and in deep learning for computer vision in particular, do checkout this wonderful compilation of available resources.

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Selected Publications

We present a comprehensive review of the state-of-the-art in automatic recognition of personality from online social networks. With our summary of the previous findings and our insights into potential research directions in this area, this work would surely serve as a good starting point for further research.
ACM Transactions on Knowledge Discovery in Data (TKDD) – (accepted for publication)

In this work we use Particle Swarm Optimization based clustering to find similar users based on their online social networking profiles, in particular Facebook.
Journal of Computers vol. 11, no. 1, pp. 18-25, 2016

Publications

  • Emerging trends in personality identification using online social networks - A Literature Survey

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  • An automation framework for configuration management to reduce manual intervention

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  • A Particle Swarm Optimization Based Approach for Finding Similar Users on Facebook

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  • Community Detection for Facebook Data of Student Hostel

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