Some Applications of Markov Chain in Python

In this article a few simple applications of Markov chain are going to be discussed as a solution to a few text processing problems. These problems appeared as assignments in a few courses, the descriptions are taken straightaway from the courses themselves.

Python For Loops Explained (Python for Data Science Basics #5)

Remember, that I told last time that Python if statements are similar to how our brain processes conditions in our everyday life? That’s true for for loops too. You go through your shopping list, until collected every item from it. The dealer gives a card for each player until everyone has five. The athlete does push-ups until reaching one-hundred… Loops everywhere! As of for loops in Python: they are perfect for processing repetitive programming tasks. In this article, I’ll show you everything you need to know about them: the syntax, the logic and best practices too!

The Value of Semi-Supervised Machine Learning

This post shows you how to label hundreds of thousands of images in an afternoon. You can use the same approach whether you are labeling images or labeling traditional tabular data (e.g, identifying cyber security atacks or potential part failures).

Importance sampling adds an interesting twist to Monte Carlo simulation

I’m contemplating the idea of teaching a course on simulation next fall, so I have been exploring various topics that I might include. (If anyone has great ideas either because you have taught such a course or taken one, definitely drop me a note.) Monte Carlo (MC) simulation is an obvious one. I like the idea of talking about importance sampling, because it sheds light on the idea that not all MC simulations are created equally. I thought I’d do a brief blog to share some code I put together that demonstrates MC simulation generally, and shows how importance sampling can be an improvement.

Microsoft R Open 3.4.3 now available

Microsoft R Open (MRO), Microsoft’s enhanced distribution of open source R, has been upgraded to version 3.4.3 and is now available for download for Windows, Mac, and Linux. This update upgrades the R language engine to the latest R (version 3.4.3) and updates the bundled packages (specifically: checkpoint, curl, doParallel, foreach, and iterators) to new versions. MRO is 100% compatible with all R packages. MRO 3.4.3 points to a fixed CRAN snapshot taken on January 1 2018, and you can see some highlights of new packages released since the prior version of MRO on the Spotlights page. As always, you can use the built-in checkpoint package to access packages from an earlier date (for reproducibility) or a later date (to access new and updated packages).


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Convolutional neural networks for language tasks

When approaching problems with sequential data, such as natural language tasks, recurrent neural networks (RNNs) typically top the choices. While the temporal nature of RNNs are a natural fit for these problems with text data, convolutional neural networks (CNNs), which are tremendously successful when applied to vision tasks, have also demonstrated efficacy in this space. In our LSTM tutorial, we took an in-depth look at how long short-term memory (LSTM) networks work and used TensorFlow to build a multi-layered LSTM network to model stock market sentiment from social media content. In this post, we will briefly discuss how CNNs are applied to text data while providing some sample TensorFlow code to build a CNN that can perform binary classification tasks similar to our stock market sentiment model.