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About me

About MeΒΆ

My journey started with Signal Processing and Communications, fields very much intertwined with Machine Learning. In fact, my first serious signal processing project was the use of K-means clustering on images (aka Vector Quantization) and later using Neural Networks for binary classification of signals from a data storage channel.

At this juncture in my career in Data Science, I feel like having come across an old friend, however now endowed with so many powerful tools and resources. Libraries developed around the R-Studio are amazing. The Python Scikit-Learn package is quite comprehensive. I really enjoy programming in Python which to me is reminiscent of Matlab (and it's free!). What I really hooked up with is the Ipython (now called Jupyter) notebook. Being able to write notes, produce some code and see the results right beneath in a report format lead to fun AND structured learning. There are so many resources out there for anybody to pick up the necessary skills to be a good data scientist.

I am truly impressed with the ecosystem built around this new exciting field. I think the power of the available material and tools is what enabled me to wade into its deep waters in such a short time. There is so much to learn, but the journey is good.

Before diving into "learning" with the "machine" word in front, I have spent the past year trying to build a language learning system in the form of a startup. To me, the process of learning is just fascinating. Teaching what you have learned in fascination is itself fascinating. Things did not work out as I had planned, but I have learned a lot along the way. The journey goes on.

I intend to post self-assigned projects on this web site using, of course, the Jupyter notebook format. Thanks to GitHub and to the wonderful python tool, Pelican, I am able to post my notebooks as web pages. If you want to be part of this journey, follow along!