"Advanced Calculus" is a free textbook, over 500 pages long, from the mathematics department of Harvard University, dedicated to an in-depth study of mathematical analysis.

It covers topics such as normed vector spaces, compactness and completeness, multi-dimensional mathematical analysis, integration, differential equations, multilinear algebra, differentiable manifolds, exterior calculus, potential theory, classical mechanics, and much more.

This is one of the most interesting resources I've shared in recent years. The book is definitely worth adding to your personal library of high-quality, freely available mathematics textbooks.

https://people.math.harvard.edu/~shlomo/docs/Advanced_Calculus.pdf

https://t.iss.one/MachineLearning9

pandas — The Mental Model. A practical, example-driven guide in 18 chapters: how tables and indexes actually work, selection and filtering, groupby, reshaping, joins, time series, text, chaining, performance, and what changed in pandas 3.0. Every example is executed, with its real output shown, and the book closes with a one-page cheat sheet.

If the field is short, cut it to: A 31-page practical guide to pandas — 18 chapters, every example executed with real output, from indexes and selection to groupby, performance and pandas 3.0.

https://t.iss.one/MachineLearning9

NumPy looks like a calculator and behaves like a memory model. 🧮

Most tutorials start with functions. This one starts with the five decisions NumPy made before you opened it: an array is one block of memory, shape is a contract between arrays, a slice is a view you can write through, the loop belongs in C, and the axis tells you which direction collapses.

15 chapters, 25 runnable examples, and every output copied from a real execution on NumPy 2.0.2. 🚀

Arrays, shapes and the machinery under the math:
https://enghusseinsheikho.substack.com/p/numpy-the-mental-model?r=3hwnr8&utmcampaign=post&utmmedium=web&showWelcomeOnShare=true