Tivadar Danka Profile picture
May 11, 2021 β€’ 9 tweets β€’ 3 min read β€’ Read on X
There is a mathematical formula so beautiful that it is almost unbelievable.

Euler's identity combines the famous numbers 𝑒, 𝑖, Ο€, 0, and 1 in a single constellation. At first sight, most people doubt that it is true. Surprisingly, it is.

This is why.

🧡 πŸ‘‡πŸ½
Let's talk about the famous exponential function 𝑒ˣ first.

Have you ever thought about how is this calculated in practice? After all, raising an irrational number to any power is not trivial.

It turns out that the function can be written as an infinite sum!
In fact, this can be done with many other functions.

For those that are differentiable infinitely many times, there is a recipe to find the infinite sum form. This form is called the Taylor expansion.

It does not always yield the original function, but it works for 𝑒ˣ.
Taylor expansions are advantageous for two reasons.

First, we can approximate functions by cutting of the sum at some N.

Second, we can simply extend functions to the complex plane with this formula!
The exponential function is not the only one that can be written as a Taylor series.

We can also do this with the trigonometric functions sine and cosine.

(Feel free to check this by hand using the general Taylor expansion formula.)
By plugging in 𝑖𝑧 into the exponential function, we discover that the complex exponential function can be written in terms of trigonometric functions!

(We use that 𝑖² = -1.)
In the special case 𝑧 = Ο€, we obtain the famous formula called Euler's identity.

This is how the magic happens.
When asked, Euler's identity often comes up among mathematicians as the most beautiful formula ever.

It is not only amazing because it connects together a bunch of famous constants, but because it establishes a connection between the exponential and trigonometric functions.
If you enjoyed this explanation, consider following me and hitting a like/retweet on the first tweet of the thread!

I regularly post simple explanations of mathematical concepts in machine learning, make sure you don't miss out on the next one!

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More from @TivadarDanka

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:

β€’ Vectors
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β€’ Equations
β€’ Factorizations
β€’ Matrices and graphs
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β€’ Eigenvalues and eigenvectors

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

Don't skip any of these:

β€’ Series
β€’ Functions
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Jul 3
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First, the story.

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Jul 3
The single biggest argument about statistics: is probability frequentist or Bayesian?

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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 Ξ©.) Image
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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
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Jul 1
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! Image
If you looked at the example above, you probably figured out the rule.

Each row is a node, and each element represents a directed and weighted edge. Edges of zero elements are omitted.

The element in the 𝑖-th row and 𝑗-th column corresponds to an edge going from 𝑖 to 𝑗.
To unwrap the definition a bit, let's check the first row, which corresponds to the edges outgoing from the first node. Image
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Jun 30
In calculus, going from a single variable to millions of variables is hard.

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This is too abstract for most engineering applications. Let's zoom in a little! Image
As our measurements are often real numbers, we prefer functions that operate on real vectors or scalars.

There are three categories:

1. vector-scalar,
2. vector-vector,
3. and scalar-vector. Image
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