There is a mathematical formula so beautiful that it is almost unbelievable.
Euler's identity combines the famous numbers 𝑒, 𝑖, π, 0, and 1 in a single constellation. At first sight, most people doubt that it is true. Surprisingly, it is.
This is why.
🧵 👇🏽
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 = π, 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.
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I regularly post simple explanations of mathematical concepts in machine learning, make sure you don't miss out on the next one!
No matter the field, you can (almost always) find a small set of mind-numbingly simple ideas making the entire thing work.
In machine learning, the maximum likelihood estimation is one of those.
I'll start with a simple example to illustrate a simple idea.
Pick up a coin and toss it a few times, recording each outcome. The question is, once more, simple: what's the probability of heads?
We can't just immediately assume p = 1/2, that is, a fair coin.
For instance, one side of our coin can be coated with lead, resulting in a bias. To find out, let's perform some statistics! (Rolling up my sleeves, throwing down my gloves.)
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?
The strength of probability theory lies in its ability to translate complex random phenomena into coin tosses, dice rolls, and other simple experiments.
So, let’s stick with coin tossing. What will the average number of heads be if we toss a coin, say, a thousand times?
To mathematically formalize this question, we’ll need random variables.
Tossing a fair coin is described by the Bernoulli distribution, so let X₁, X₂, … be such independent and identically distributed random variables.
The expected value is one of the most important concepts in probability and statistics.
For instance, all the popular loss functions in machine learning, like cross-entropy, are expected values. However, its definition is far from intuitive.
Here is what's behind the scenes.
It's better to start with an example.
So, let's play a simple game! The rules: I’ll toss a coin, and if it comes up heads, you win $1. However, if it is tails, you lose $2.
Should you even play this game with me? We’ll find out.
After n rounds, your earnings can be calculated by the number of heads times $1 minus the number of tails times $2.
If we divide total earnings by n, we obtain your average earnings per round.
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.
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.
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.)
If the sidewalk is wet, is it raining? Not necessarily. Yet, we are inclined to think so. This is a preposterously common logical fallacy called "affirming the consequent".
However, it is not totally wrong. Why? Enter the Bayes theorem.
Propositions of the form "if A, then B" are called implications.
They are written as "A → B", and they form the bulk of our scientific knowledge.
Say, "if X is a closed system, then the entropy of X cannot decrease" is the 2nd law of thermodynamics.
In the implication A → B, the proposition A is called "premise", while B is called the "conclusion".
The premise implies the conclusion, but not the other way around.
If you observe a wet sidewalk, it is not necessarily raining. Someone might have spilled a barrel of water.