Because humans can't see in more than 3D, it is challenging to make sense of it for the first time. However, there is a simple yet beautiful pattern behind.
This is how the magic is done!
What is a cube in one dimension?
It is simply two vertices connected with a line of unit length.
To move beyond and construct a cube in two dimensions, also known as a square, we simply copy a one-dimensional cube and connect each original vertex with its copy.
(These new edges are colored blue.)
You can probably guess the pattern by now.
Copying a cube's graph and connecting each of its vertices with its corresponding copy brings it to the next dimension.
This is how it looks in 3D.
Repeating this process one more time, we obtain a tesseract, that is, a cube in four dimensions.
(I stretched the new edges a bit to make it easier to see the pattern.)
All 8 of its faces are 3D cubes.
I have always found this pattern quite beautiful.
Geometry intrigued me since I was a child, and when I discovered how to draw a tesseract, I was over the moon.
Small things such as this ignited my desire to be a mathematician, and I still enjoy playing around with fun math.
I regularly post deep dive threads about mathematics and machine learning, explaining (seemingly) complex concepts in a simple way.
If you also love to look beyond the surface and understand how things work, consider giving me a follow!
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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.