A few years ago, I remember being faced with an introductory physics problem. It was the first time I had encountered the idea of acceleration, and I was puzzled by the notation for it.

Applying the Bayes’ Rule to design a classifier in Python from scratch, and applying it on the Titanic Dataset — This article explains the probability theory that underlies the concept of Naive Bayes’, so if you’re looking for a theoretical understanding, see that. Naive Bayes Classification I: Theory How we can use elementary probability theory to find Bayes’ Theorem, and how we can use this to create a classifiermedium.datadriveninvestor.com

How we can use elementary probability theory to find Bayes’ Theorem, and how we can use this to create a classifier — If you are looking for the practical implementation of a Naive-Bayes model from scratch, part II of this article explains that. I highly encourage you to read this first to understand the theory: Naive Bayes Classifiers II: Application Applying the Bayes’ Rule to design a classifier in Python from scratch, and applying it on the Titanic Datasetadamdhalla.medium.com

Exploring Recursive Least Squares (RLS) and using the Sherman-Morrison-Woodbury Formula and Python — The mathematics here should be tackled with individuals who have completed an introductory linear algebra course. For those just looking for the code implementation, visit the GitHub repository here. The Normal Equations for Least Squares are ubiquitous and for good reason. Apart from very large datasets, the Normal Equations are…

Using Python and some basic linear algebra concepts, we can balance chemical equations — I’ll outline my theoretical approach to the problem in python here, using only some code, and you can get an idea of what is going on. I’ll link to the code on GitHub for the actual code. Balancing chemical equations is a common activity in high-school classrooms and beyond. …

Using two different strategies rooted in linear algebra to understand the most important formula in dimensionality reduction — This article assumes the reader is comfortable with the contents covered in any introductory linear algebra course — orthogonality, eigendecompositions, spectral theorem, Singular Value Decomposition (SVD)… Confusion of the proper method to do Principal Component Analysis (PCA) is almost inevitable. Different sources espouse different methods, and any learner quickly deduces…

This one’s a little unusual of a post since I don’t have any stories, algorithms, math or philosophy to write about, but I spent the past week putting together a complete lecture on the mathematics that underlie neural networks. It’s about 5 hours long, but there in the description there are timestamps to every subject covered in the video, which is in the twenties or thirties. Here’s the syllabus of covered topics if you’d like.

What is a perceptron, how the proto-neural network started (and stopped) interest in neural networks, the linear algebra behind them, and how group invariance theorem destroyed them. — If you were to gather a group of scientists from 1962 and ask them about their outlooks on the future and potential of artificial intelligence in solving computationally hard problems, the consensus would be generally positive.

The rationale and linear algebra behind Normal Equations, and the calculus as well. — This is a continuation of my Linear Algebra series, which should be viewed as an extra resource while going along with Gilbert Strang’s class 18.06 on OCW. This can be closely matched to Lecture 16 his series. This article requires understanding of the four fundamental subspaces of a matrix, projection…