Logistic Regression from the ground up
Logistic regression from first principles. Focus is on model formulation, log-odds interpretation, regularisation, gradient and Hessian derivations, and the multinomial case.
Sjmelck Just Means Engineers Love Computers, K?
Sjmelck
Practical guides to signal processing, machine learning, optimisation and scientific computing in Python. Sjmelck just means engineers love computers, K?
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Latest posts
An introduction to Radial basis functions
How radial basis function (RBF) surrogate models work, and how to implement a Gaussian RBF interpolation and regression model in Python with NumPy and SciPy.
Introduction to Latex With Assistance From Zotero
How to set up Zotero with Better BibTeX to keep one .bib file in sync for LaTeX referencing, plus TeXstudio settings and finding papers with Research Rabbit.
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What we write about
Learning-based methods
Statistical learning, machine learning, and deep learning.
Signal processing
Practical explanations of signals, transforms, and diagnostics.
Python
Scientific computing, visualisation, and reproducible workflows.
Optimisation
Models and methods for solving engineering problems.
A personal favourite
Intuitions behind relative velocity
