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Markov Chain Algorithm for Sentence Generation
The Markov chain algorithm can be used to generate sentences by starting with a few words and using auto-complete to build out the sentences. The algorithm is relatively small but commonly used in teaching different concepts such as optimization. It is a neat experiment for those interested in probabilities and can also be used for mad libs fun. The implementation of the algorithm is concise with only 24 lines including comments.
How does a debugger work? What can you learn about Python by building one from scratch? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.
Christopher shares a two-part tutorial on building a debugger in Python? Creating a simple one requires less code than you might think.
We also talk about an article from Itamar Turner-Trauring about how to prepare for the upcoming changes to NumPy. The new version is not backward compatible and will require some inspection of your project dependencies. Itamar includes advice, techniques, and tools for updating your code.
We also share several other articles and projects from the Python community, including a couple of news items, a discussion about managing advice as a new developer, moving to Python as a former R developer, building a Markov chain to generate readable nonsense, optimizing fonts to individual glyphs on your website, and a project for working with units of measurement in Python.
This week’s episode is brought to you by Posit Connect.
Course Spotlight: Create a Tic-Tac-Toe Python Game Engine With an AI Player
In this video course, you’ll create a universal game engine in Python for tic-tac-toe with two computer players, one of which will be an AI player using the powerful minimax algorithm. You’ll give your game library a text-based graphical interface and explore two front ends.
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