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Mathematics of Machine Learning

I met Tivadar during Covid.

We were all stuck at home, unsure what to do with all the extra time, so we started talking about building something together.

I wanted to teach people Machine Learning. I had this idea about building a website that would ask random questions for people to answer. I wanted the site to do a hundred different things, but one thing was non-negotiable: I wanted people to leave feeling they had learned something different.

Tivadar was the answer to that.

Machine Learning is tough, and unfortunately, most educational content you find online suffers from chronic handwaving syndrome: overused buzzwords, skipped intuition, and more confusion than when you started.

At the time, Tivadar was already writing online about math. He wasn't the only one, but he was different. He was taking seemingly mundane topics and telling a story around them that was surprisingly effective.

There wasn't any handwaving or burying people under a mountain of theoretical ideas. The writing was different, sharp, and fresh.

I had never been excited about math before. I read every single one of Tivadar's posts. I wasn't just learning the rules—I was learning how to think. And—shockingly—I was entertained.

I had never seen that combination before.

I asked Tivadar to help me with the site, and he did—for a while until he decided to move on to start writing this book. I remember telling him I understood, but I was secretly sad. Really sad.

Today, I'm thrilled this happened the way it did.

Mathematics of Machine Learning is the inevitable consequence of those short posts that excited me about math for the first time. It's not just the best book I've read on the subject—it's the one I wish existed when I started.

This book does something rare: it teaches you the math behind machine learning without boring you with vague concepts—or making you forget why you showed up in the first place.

The book is laser-focused on what you need and says nothing about what you don't. The explanations are vintage Tivadar: sharp, detailed, and entertaining. You can't just read or memorize them; you'll understand them.

I've been reading this book since it was an idea and a bunch of notes and sketches. I've watched it grow from online posts to something polished and powerful. And I've learned a lot—not just about math, but about how to explain math.

I'll leave you to it. You're in for a treat. Enjoy the journey. I know I did.