Naive Bayes is one of the simplest classification machine learning algorithm. As the name suggests its based on the Bayes theorem.
Doing my thesis using Probabilistic Programming I always had read about many models and how it compared with Naive Bayes classifier. Even though of the simplicity Naive bayes is a pretty solid and basic classifier every machine learning student should know. But I never had an opportunity to fully understand this simple tool mainly because I used it like a blackbox using many implementations available, the most famous being from scikit learn
There is a lot of ink spent on the topic by most prominent scholars of all time so I really dont want to add anything to the debate.
The 1 paper which influenced me in getting a practical overview of the topic and showing tangiblly was by Jake Vanderplass , where he explains with 2 examples
I've been using SymPy in my research and coursework for a while now. For those that don't know, SymPy is a computer algebra system, capable of performing symbolic calculations that would be too complicated to do by hand. Which makes it perfect for solving the equations needed to generate the equations of motion (EOM) of multibody systems! In this post, I'll demonstrate a simple workflow for generating the EOM for a differential drive robot.
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