Tivadar Danka Profile picture
Nov 3, 2022 18 tweets 6 min read Read on X
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.
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.
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.
The wise man says "hold my camel", and solves the problem by lending one to the boys.

Now the stable has 18. The eldest son takes 9 home, while the middle and smallest son leaves with 6 and 2, as their father wished.

The wise man takes his camel back, and everybody is happy.
Thus, the camel principle is born: adding and subtracting the same quantity doesn't change the equality, but can help in the computation.

In mathematics, you cannot live without this principle.

I'll show you two examples.
The first one is the quadratic equation.

Its solution formula is one of the few things that everybody remembers from high school. Even if they are woken up in the middle of the night.

This formula is derived from the camel principle. Let me show you how!
After factoring out 𝑎 from the equation, we notice that the famous identity

(α + β)² = α² + 2αβ + β²

might help to factor the quadratic equation into a product.

To achieve that, we apply the camel principle!
After adding and subtracting the same quantity, the terms with 𝑥 factor into a product.
This leads straight to the solution formula.
There is an alternative version of the camel principle, performing a similar feat: multiplying and dividing with the same quantity.

This doesn't change the equality either.
To illustrate, let's look at derivatives, the main engine behind mathematics, physics, and optimization.

(And tons of other fields that allowed technology to get where it is now.)
How would you calculate the derivative of a composite function?

This is a quintessential question. Without this, you don't have backpropagation, gradient descent, and thus neural networks.

(At least until someone invents a clever alternative. But that'll take a while.)
You guessed right: the camel principle!

(At least, the second version, where you multiply and divide with the same quantity.)
After the camel principle is applied, the limit can be carried out termwise.
(For those with the eagle's eyes: yes, the denominator can be zero. You can epsilon-delta your way out of that, but I won't do it here.)
And thus, we have the chain rule, one massive pillar of science and technology.

This is what we use to perform backpropagation, enabling us to train our neural networks in a reasonable time.
The lesson here: tiny mathematical curios such as the camel principle are often dismissed as "lacking any applications".

However, such short-sightedness frequently leads astray.

By understanding atoms, you are able to build skyscrapers.
If you have enjoyed this explanation, share it with your friends and give me a follow! I regularly post deep-dive explainers such as this.

Understanding mathematics will make you a better engineer, and I want to help you with that.

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More from @TivadarDanka

May 7
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
Mar 22
I am Hungarian, living in Hungary for 35 years. Everything is government propaganda in this thread.

Let me provide all the context.

Learn from this, and maybe your country can succeed in stopping an authoritarian takeover, in which Hungary have failed.

"1. No income tax for women with at least two children for life."

This is an election hack, meant to buy votes for the upcoming 2026 election. Fidesz (Hungary's ruling party) is significantly down in the polls after it was leaked that a convicted p*d*ph*le accessory was given a presidential pardon.

Hell, they even let a child p*rn*gr*phy wholesaler with 96000 images on his computer walk away with ~$1500 fine. (Check en.wikipedia.org/wiki/G%C3%A1bo… if you don't believe me.)

Thus, the government is scraping to buy back the trust of families.

Even if it wasn't an empty promise, waiving the income tax is unrealistic for budgetary reasons. Hungary's economy is in the toilet.
"3. Housing incentives for young couples.

Offers a low interest loan for couples raising or committing to having one child or more."

This loan is another propaganda trick. In practice, this loan resulted in the biggest housing crisis of the country's history, because all it did was raise the price of every real estate by the amount of the loan, making real estate ownership virtually impossible for the young generation.
Read 10 tweets
Feb 28
I am an evangelist for simple ideas.

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. Image
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.)
Read 28 tweets
Feb 26
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? Image
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. Image
Read 17 tweets
Feb 24
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. Image
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. Image
Read 16 tweets
Feb 21
You have probably seen the famous bell curve hundreds of times before.

It is often referred to as some sort of “probability”. Contary to popular belief, this is NOT a probability, but a probability density.

What are densities and why do we need them? Image
First, let's talk about probability.

The gist is, probability is a function P(A) that takes an event (that is, a set), and returns a real number between 0 and 1.

The event is a subset of the so-called sample space, a set often denoted with the capital Greek omega (Ω). Image
Every probability measure must satisfy three conditions: nonnegativity, additivity, and the probability of the entire sample space must be 1.

These are called the Kolmogorov axioms of probability, named after Andrey Kolmogorov, who first formalized them. Image
Read 21 tweets

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