Before we begin, let us talk about how Mike (a fictional character) spends a typical morning. Mike begins his day by searching for breakfast recipes on Google Now (https://en.wikipedia.org/wiki/Google_Now). After a filling breakfast, Mike starts getting ready for work. He asks Siri (http://www.apple.com/in/ios/siri/) to tell him the weather and traffic conditions for his drive to work. Finally, as Mike gets ready to leave the house, he asks Alexa (https://en.wikipedia.org/wiki/Amazon_Alexa) to dim the lights and thermostat. It is not even 10 a.m. yet, but Mike like many of us has already used three intelligent personal assistant applications using Natural Language Processing (NLP). We will unravel the mysteries of building intelligent personal assistants with a simple example to build such an assistant quite easily using NLP.
Python is a very powerful programming language that understands structural, functional and object oriented programming paradigms. New comers to Python from other languages tend to carry with them their mother (programming) tongue culture. Although they achieve the required task, they might have fallen in the trap of using Python the wrong way. In this post, we cover some efficient tricks to achieve tasks in Python; we call it the Pythonic way. Find an IPython Notebook for all tricks here on our GitHub repository.
Lists, Tuples, Dictionaries and Sets
Artificial Neural Networks (ANN) are computational models inspired from one of nature’s most splendid creations – the neuron. It seems our quest to make the machines smarter has converged onto the realization that we ought to code the ‘smartness’ into them, literally. What better way than to draw parallels from the source of our own intelligence, our brains?
Some months ago I attended a presentation where one of my colleagues, Panos, showed how he used Python to process data in a meaningful way. In particular, he showed how he extracted some interesting findings from a .csv file coming from the Boston Mayor’s 24 Hour Constituent Service web site. Such findings involved incidents that were still open by then, how many incidents were closed in a justifiable amount of time and others. Continue reading