Logistic Regression is the most important foundational algorithm in Classification Modeling. In 2 minutes, I'll crush your confusion. Let's dive in:

This guy literally explains how to build your own ChatGPT (for free)

Logistic regression is one of the simplest models in machine learning, and one of the most revealing. It shows us how to move from geometric intuition to probabilistic reasoning. Mastering it sets the foundation for everything else. Let’s dissect it step by step!

In machine learning, we take gradient descent for granted. We rarely question why it works. What's usually told is the mountain-climbing analogue: to find the valley, step towards the steepest descent. But why does this work so well? Read on:

The Law of Large Numbers is one of the most frequently misunderstood concepts of probability and statistics. Just because you lost ten blackjack games in a row, it doesn’t mean that you’ll be more likely to be lucky next time. What is the law of large numbers, then? Read on:

(Inutile thread. Per curiosità sono andato a sentire e a leggere quanto detto da Carlo Rovelli sulla nascita dell'arma atomica e su Enrico Fermi e, sinceramente, non ho trovato nulla di quello che gli si ascrive in queste ore di futile polemica. ->

The Infinite Nature of Prime Numbers - a Thread🧵 Prime numbers have fascinated mathematicians for over centuries. The basic idea behind Prime numbers is that these numbers are divisible only by 1 and themselves. What looks completely random has a unique pattern hidden in👇

Stanford's Courses on AI & ML (FREE): ❯ CS221 - Artificial Intelligence ❯ CS229 - Machine Learning ❯ CS230 - Deep Learning ❯ CS234 - Reinforcement Learning ❯ CS229M - ML Theory ❯ CS224U - NL Understanding ❯ CS224N - NLP with Deep Learning All links inside:

You have seen the famous bell curve hundreds of times before. Contrary to popular belief, this is NOT a probability, but a probability density. What are densities, and why do we need them? Read on:

The most important AI paper of 2025 might have just dropped. NVIDIA lays out a framework for Small Language Model agents that could outcompete LLMs. Here’s the full breakdown (and why it matters):

That's it — I couldn’t take it anymore! After getting so many requests from students for 3D methods in GeostatsPy, I finally caved. I stayed late on campus a couple of nights and wrote the algorithms to support students and professionals around the world who need 3D

Machine Learning by Andrew Ng - from Stanford Complete lecture notes from 2023.

Python unlocks the power of mathematics, the universe's language, allowing students and professionals to explore complex concepts and solve real-world problems through code. by Python Coding Grab your copy: gumroad.com/a/215786643/vw…

To help my students better understand Principal Component Analysis (PCA) for feature dimensionality reduction, I created this interactive dashboard using #Python and Matplotlib. + Add a new point, drag it around, and watch it transform step by step through the PCA pipeline: ☐

Harvard's Applied Math 205: "Advanced Scientific Computing: Numerical Methods" Video Lectures: youtube.com/playlist?list=… Course Material: people.math.wisc.edu/~chr/am205/mat…

New paper now out in Physics Report: If you're interested in information theory, complex systems, and emergent properties, this is the paper for you: a comprehensive review paper designed to bring anyone up to speed on information theory for complex systems scientists.

I crossed an interesting threshold yesterday, which I think many other mathematicians have been crossing recently as well. In the middle of trying to prove a result, I identified a statement that looked true and that would, if true, be useful to me. 1/3

Stanford's Courses on AI & ML (FREE): ❯ CS221 - AI ❯ CS229 - ML ❯ CS229M - ML Theory ❯ CS230 - DL ❯ CS234 - RL ❯ CS236 - Deep Generative Models ❯ CS336 - LLM from Scratch ❯ CS224N - NLP with DL Course links inside:

Arguably one of the most important mathematical papers in modern times

I built Multi-Head Attention in Excel because I wanted to understand how it works. It helped me — now it can help you too. 🔽 Download: byhand.ai/mha