Markov Chains In Python

Sandipan Dey shows off various uses of Markov chains as well as how to create one in Python:

Perspective. In the 1948 landmark paper A Mathematical Theory of Communication, Claude Shannon founded the field of information theory and revolutionized the telecommunications industry, laying the groundwork for today’s Information Age. In this paper, Shannon proposed using a Markov chain to create a statistical model of the sequences of letters in a piece of English text. Markov chains are now widely used in speech recognition, handwriting recognition, information retrieval, data compression, and spam filtering. They also have many scientific computing applications including the genemark algorithm for gene prediction, the Metropolis algorithm for measuring thermodynamical properties, and Google’s PageRank algorithm for Web search. For this assignment, we consider a whimsical variant: generating stylized pseudo-random text.

Markov chains are a venerable statistical technique and formed the basis of a lot of text processing (especially text generation) due to the algorithm’s relatively low computational requirements.

Related Posts

Plotting ML Results In R

Bernardo Lares shows off the plots he creates in R to compare ML models: Split and compare quantiles This parameter is the easiest to sell to the C-level guys. “Did you know that with this model, if we chop the worst 20% of leads we would have avoided 60% of the frauds and only lose […]

Read More

Building TensorFlow Neural Networks On Spark With Keras

Jules Damji has an example of using the PyCharm IDE to use Keras to build TensorFlow neural network models on the Databricks MLflow library: Our example in the video is a simple Keras network, modified from Keras Model Examples, that creates a simple multi-layer binary classification model with a couple of hidden and dropout layers and […]

Read More

Categories

January 2018
MTWTFSS
« Dec Feb »
1234567
891011121314
15161718192021
22232425262728
293031