Where does our fear of failure come from? For me, it’s probably from my earliest years in elementary school.

The single most undervalued fact of linear algebra: matrices are graphs, and graphs are matrices. Encoding matrices as graphs is a cheat code, making complex behavior simple to study. Let me show you how!

At some point everyone will tell you that you can do this alone. You got this. You can figure this out. You're smart. It turns out I rather surround myself with people whose skillset is as disjoint from mine as possible. You need to kill your ego if you want to grow.

Visual representation of the behavior of the DFS algorithm. It tries to explore new unvisited vertices until it is stuck. Then, it backtracks to the closest vertex with an adjacent non-visited vertex left and continues exploring.

A stage of the generic algorithm for finding the connected component of S. Red vertices represent the current state of set D. Discontinued lines have one endpoint in the set D and the other outside of it. Nodes S and T belong to different connected components.

If there's something I'm proud of in 2025 is the reception of my book, Mathematics of Machine Learning. With a whooping 5000+ sales across all formats, I'm humbled. Thanks to everyone of you for your interest in the fundamentals of math & machine learning.

If you are indulging yourself this Christmas, I got good news for you! The digital version of my new book, Mathematics of Machine Learning is now $9.99 for a limited time. Get your hands on it if you're upping your machine learning game in 2026: www.packtpub.com/en-us/produ...

The 10 most popular posts of The Palindrome in 2025 (chosen by you): 1. The Roadmap of Mathematics for Machine Learning: thepalindrome.org/p/the-roadm...

I had to come up with a way of visualizing the chain rule because I could never remember the formula. Here I show you all about it: thepalindrome.org/p/visualizi...

One of the coolest ideas in mathematics is the estimation of a shape's area by throwing random points at it. Don't believe this works? Check out the animation below, where I show the method on the unit circle. (Whose area equals to π.) Here is what's behind the magic:

I wrote a 4000-words long article about all the math you need to know for machine learning. Trust me, you want to bookmark this: thepalindrome.org/p/the-roadm...

The singlemost useful realization to achieve all your goals: If you want to hit your target, take more shots. Here's the science-backed article: thepalindrome.org/p/if-you-wa...

The single most life-altering realization I've ever had is buried under a heap of probability theory. Every mathematician is familiar with the idea. They call it “trivial”, move on to study virtuoso mathematical feats, and spend no time pondering the consequences of such short computation.

The Palindrome now has 100+ deep dives on math & ML. So I organized them into a single “library” page where you can: 1. Pick a learning track 2. Find the right article in seconds 3. Start learning right away Bookmark it. Future-you will thank you: thepalindrome.org/p/the-palin...

If I had to learn Math for Machine Learning from scratch, this is the roadmap I would follow:

The single most undervalued fact of linear algebra: matrices are graphs, and graphs are matrices. Encoding matrices as graphs is a cheat code, making complex behavior simple to study. Let me show you how!

Behold one of the mightiest tools in mathematics: the camel principle. I am dead serious. Deep down, this tiny rule is the cog in many methods. Ones that you use every day. Here is what it is, how it works, and why it is essential:

Matrix multiplication is not easy to understand. Even looking at the definition used to make me sweat, let alone trying to comprehend the pattern. Yet, there is a stunningly simple explanation behind it. Let's pull back the curtain!

Experimenting with white backgrounds for the latest post of The Palindrome. What do you think? Should I stick to it or go back to dark?

The latest post on The Palindrome is all about teaching you thee math behing your commute. And it comes with a brand new visual style. I need your feedback on this one, so please give it a read here: thepalindrome.org/p/the-math-...

I wrote a 1500-word long article to teach you the real process of publishing a technical book in today's AI/ML landscape. Trust me, if you're thinking about publishing your own book, this will save you time and money: thepalindrome.org/p/the-story...

Writing online about mathematics and machine learning started as a creative hobby for me, a way to recharge my batteries after burning my savings and two years of my life. It turned out to be the most rewarding decision I have ever made: thepalindrome.org/p/the-story...

If there's one realization that will help you level up faster than your peers is this: There are no stupid questions. This is why "good students" will never make great minds: thepalindrome.org/p/there-are...

I wrote a 1000-word long article to teach how derivatives work with one, two, and a Billion variables. Trust me, no one has ever explained derivatives to you like this before: thepalindrome.org/p/derivativ...

Many of you have asked me for the singlemost useful FREE resource I've written. It's the Roadmap of Mathematics for Machine Learning, without question. Enjoy: thepalindrome.org/p/the-roadm...

Mathematics of Machine Learning just crossed 5K sales! And you can get the book now at 15% discount on Amazon.

The single most undervalued fact of mathematics: mathematical expressions are graphs, and graphs are matrices. Yes, I know. You already heard this from me, but hear me out. Viewing neural networks as graphs is the idea that led to their success.

The single biggest argument about statistics: is probability frequentist or Bayesian? It's neither, and I'll explain why. Buckle up. Deep-dive explanation incoming.

Matrix multiplication is not easy to understand. Even looking at the definition used to make me sweat, let alone trying to comprehend the pattern. Yet, there is a stunningly simple explanation behind it. Let's pull back the curtain!

Behold one of the mightiest tools in mathematics: the camel principle. I am dead serious. Deep down, this tiny rule is the cog in many methods. Ones that you use every day. Here is what it is, how it works, and why it is essential:

If I had to learn Math for Machine Learning from scratch, this is the roadmap I would follow:

The single most undervalued fact of linear algebra: matrices are graphs, and graphs are matrices. Encoding matrices as graphs is a cheat code, making complex behavior simple to study. Let me show you how!

This is the question I get asked the most as a machine learning practitioner: How do you make your illustrations? I break down the whole process here: thepalindrome.org/p/how-do-i-...

The single most important skill for learning how to code is coding on paper: thepalindrome.org/p/coding-on...

I wrote a 1500-word article to teach you the anatomy of the least squares method. Trust me, most people overlook this powerful tool. And you would want to bookmark this: thepalindrome.org/p/the-anato...

The way you think about the exponential function is wrong. Don't think so? I'll convince you. Did you realize that multiplying e by itself π times doesn't make sense? Here is what's really behind the most important function of all time:

I wrote a 1000-word long article to teach you the difference between probabilities, statistics, and machine learning. Trust me, you want to bookmark this: thepalindrome.org/p/machine-l...

The single most undervalued fact of mathematics: mathematical expressions are graphs, and graphs are matrices. Yes, I know. You already heard this from me, but hear me out. Viewing neural networks as graphs is the idea that led to their success.

I’m convinced mathematics is taught wrong. Its beauty is buried under technical jargon and lifeless formulas. It should be be: clear, visual, practical. My mission is to get you fluent in the math that will still be useful in 50 years. thepalindrome.org/p/the-palin...

Matrix multiplication is not easy to understand. Even looking at the definition used to make me sweat, let alone trying to comprehend the pattern. Yet, there is a stunningly simple explanation behind it. Let's pull back the curtain!

I wrote a 4000-words long article about all the math you need to know for machine learning. Trust me, you want to bookmark this: thepalindrome.org/p/the-roadm...

One of the coolest ideas in mathematics is the estimation of a shape's area by throwing random points at it. Don't believe this works? Check out the animation below, where I show the method on the unit circle. (Whose area equals to π.) Here is what's behind the magic:

They said “fundamentals don’t grow newsletters.” We doubled down anyway: • 35,000+ readers • 541 premium members • $44,606 Annual Recurring Revenue Sometimes the unsexy path is the one with the strongest compounding.

I used to hate coding on paper as a student. It’s linear, uninteractive, and tedious. Coding on paper felt like a punishment. Was there a point? I used to believe there wasn’t; now I know there is: thepalindrome.org/p/coding-on...

If you want to become unstoppable in machine learning, master this minimal set of math first: Linear Algebra → vectors, matrices, and their operations Calculus → limits, differentiation, integration Probability → uncertainty, Bayes, distributions Optimization → gradient descent and friends

Everyone is chasing AI headlines. I’m teaching the fundamentals that make those headlines possible. I wrote a full explainer on the math that’s beneath all of ML. Trust me, you’ll want to bookmark this: thepalindrome.org/p/the-roadm...

I’m convinced mathematics is taught wrong. Its beauty is buried under technical jargon and lifeless formulas. It should be be: clear, visual, practical. My mission is to get you fluent in the math that will still be useful in 50 years. thepalindrome.org/p/the-palin...

I wrote a 1500-word article to teach you the anatomy of the Least Squares method. Trust me, you want to bookmark this: thepalindrome.org/p/the-anato...

You kept asking for it so I finally put all my articles in a single place. Here's your machine learning library. Trust me, you want to bookmark this. It's only going to grow from here on: thepalindrome.org/p/the-palin...

I wrote a 1000-word long article to teach you the difference between probabilities, statistics, and machine learning. Trust me, you want to bookmark this: thepalindrome.org/p/machine-l...