Tivadar Danka Profile picture
Jul 11 12 tweets 3 min read Read on X
Most people think math is just numbers.

But after 20 years with it, I see it more like a mirror.

Here are 10 surprising lessons math taught me about life, work, and thinking clearly: Image
1. Breaking the rules is often the best course of action.

We have set theory because Bertrand Russell broke the notion that “sets are just collections of things.”
2. You have to understand the rules to successfully break them.

Miles Davis said, “Once is a mistake, twice is jazz.”

Mistakes are easy to make. Jazz is hard.
3. Understanding happens when you take things slow.

Most of my classes were in classic chalk + blackboard style.

So we took notes of...

• formulas
• theorems
• definitions

...from a blackboard, which was more beneficial than relying on a PowerPoint presentation.
4. The best way to learn is to solve problems.

I just said that “understanding happens when you take things slow.”

Well, the easiest way to take things slow is by being forced to by pushing your skills and knowledge to the limit.
5. There are no shortcuts to mastery.

You can’t pay your way to knowledge.

You must put in the work for math and anything else that’s worth doing.
6. Always tackle one issue at a time.

Look at special cases, then add complexity step by step. Image
7. Finding the right perspective is half the success.

• Matrices or graphs.
• Algebraic expressions or vectors.
• Driving your startup to the ground or a learning opportunity.

Success and failure are matters of perspective: you win, or you learn. Image
8. Asking questions is a superpower.

“It’s not that there are no stupid questions,” the words of my professor echo in my ear, “it’s that not asking your questions is stupid.”
9. Talent is just the icing on the cake.

The rest is hard work and perseverance.

I'm no genius, I am:

• curious,
• hard-working,
• and emotionally resilient,

Without these, no amount of brilliance can put you at the top or even in the middle of the pack.
10. Don’t give too much credit to advisors and professors.

They are people like you, and experience is the only thing they have over you.
If this post sparked some love for math & ML, you will love my new book.

When you buy it, you also get access to:

• GitHub repo with all the code
• FREE digital copy of the book (PDF)
• Discord community where you can talk to me directly

Get it now: amazon.com/Mathematics-Ma…

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More from @TivadarDanka

Jul 8
This will surprise you: sine and cosine are orthogonal to each other.

What does orthogonality even mean for functions? In this thread, we'll use the superpower of abstraction to go far beyond our intuition.

We'll also revolutionize science on the way. Image
Our journey ahead has three milestones. We'll

1. generalize the concept of a vector,
2. show what angles really are,
3. and see what functions have to do with all this.

Here we go!
Let's start with vectors. On the plane, vectors are simply arrows.

The concept of angle is intuitive as well. According to Wikipedia, an angle “is the figure formed by two rays”.

How can we define this for functions? Image
Read 18 tweets
Jul 7
In machine learning, we use the dot product every day.

However, its definition is far from revealing. For instance, what does it have to do with similarity?

There is a beautiful geometric explanation behind. Image
By definition, the dot product (or inner product) of two vectors is defined by the sum of coordinate products. Image
To peek behind the curtain, there are three key properties that we have to understand.

First, the dot product is linear in both variables.

This property is called bilinearity. Image
Read 15 tweets
Jul 5
If I had to learn Math for Machine Learning from scratch, this is the roadmap I would follow: Image
1. Linear Algebra

These are non-negotiables:

• Vectors
• Matrices
• Equations
• Factorizations
• Matrices and graphs
• Linear transformations
• Eigenvalues and eigenvectors

Now you've learned how to represent and transform data. Image
2. Calculus

Don't skip any of these:

• Series
• Functions
• Sequences
• Integration
• Optimization
• Differentiation
• Limits and continuity

Now you understand the math behind algorithms like gradient descent and get a better feeling of what optimization is. Image
Read 6 tweets
Jul 3
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. Image
First, the story.

The old Arab passes away, leaving half of his fortune to his eldest son, third to his middle son, and ninth to his smallest.

Upon opening the stable, they realize that the old man had 17 camels. Image
This is a problem, as they cannot split 17 camels into 1/2, 1/3, and 1/9 without cutting some in half.

So, they turn to the wise neighbor for advice. Image
Read 18 tweets
Jul 3
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. Image
First, let's look at what is probability.

Probability quantitatively measures the likelihood of events, like rolling six with a dice. It's a number between zero and one.

This is independent of interpretation; it’s a rule set in stone. Image
In the language of probability theory, the events are formalized by sets within an event space.

The event space is also a set, usually denoted by Ω.) Image
Read 33 tweets
Jul 2
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! Image
First, the raw definition.

This is how the product of A and B is given. Not the easiest (or most pleasant) to look at.

We are going to unwrap this. Image
Here is a quick visualization before the technical details.

The element in the i-th row and j-th column of AB is the dot product of A's i-th row and B's j-th column. Image
Read 16 tweets

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