Looking for guest bloggers

Hi there folks! I am very busy now-a-days. You might already be aware of that due to the long pauses between posts. Therefore, I am searching for guest bloggers who would like to write about Python, it’s frameworks or literally anything interesting and informative related to Python. Continue reading

help, inspiration, news

Nice tutorials I bumped into recently

Hi guys! I am in the USA right now. I am here to attend a Hackathon at the University of Michigan called MHacks 6. This is my first ever Hackathon so I am very excited. So now without wasting any time let’s get down to business. I recently came across a lot of nice tutorials which I believe might be useful for you guys. I am listing them below in no particular order: Continue reading

Featured Image -- 658

Generating Text Using a Markov Model

Originally posted on alexhwoods:

Screen Shot 2015-08-05 at 10.57.18 AM

A Markov Chain is a random process, where we assume the previous state(s) hold sufficient predictive power in predicting the next state. Unlike flipping a coin, these events are dependent. It’s easier to understand through an example.

Imagine the weather can only be rainy or sunny. That is, the state space is rainy or sunny. We can represent our Markov model as a transition matrix, with each row being a state, and each column being the probability it moves to another.

f5f65f94c2f9cd72f9f2ce10499e6b7b However, it’s easier to understand with this state transition diagram.


In other words, given today is sunny, there is a .9 probability that tomorrow will be sunny, and a .1 probability that tomorrow will be rainy.

Text Generator

One cool application of this is a language model, in which we predict the next word based on the current word(s). If we just predict based on the last word…

View original 507 more words