A big problem with social and political sciences is that they *look* so intuitive and approachable that literally everyone has an opinion.
If I say "this is how quantum entanglement works" almost no one will dare to reply.
But if I say "this is how content moderation works"...
And the thing is, there is huge amount of actual, solid science on almost any socially relevant topic, and most of us are as uninformed in that as we are on any dark corner of particle physics.
We just believe we can have an opinion, because the topic seems less objective.
So we are paying a huge disrespect to social scientists, who have to deal every day with the false notion that what they have been researching for years is something that anyone, thinking for maybe five minutes, can weigh in. This is of course nonsense.
And don't get me wrong, you're entitled to have any opinions you want on any topics you care, be it society of the inner workings of the universe.
Keep in mind, though, that without deeply studying about it, your opinions are as likely to be pure nonsense in either case.
And this is not gatekeeping. I'm not saying you need to have a degree or any formal education before you're allowed to discuss seriously about any topic.
But you do need to be responsible enough to at least spend a few hours catching up on the relevant information about it.
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❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵
Last time we talked about how Machine Learning works.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric M.
According to the nature of that experience, we can define different formulations, or flavors, of the learning process.
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇
I've been a vocal opponent of the "neural networks are brain simulations" analogy, not because it's *wrong* but because I believe it's harmful for beginners.
I want to propose an alternative analogy for approaching deep learning from a dev background.
👇
Think about detecting a face in an image.
How would you even start to write a program for that?
You know it's gonna have something to do with finding a "nose" and two "eyes", but how can you go from an array of pixels to something that looks like an eye, in whatever position?
Now, suppose you have access to thousands of faces and non-faces.
How does that changes the problem?
Instead of thinking in the problem domain (finding faces) you can now take a leap upwards in abstraction, and think in the meta-problem domain (finding face finders).
A day to share with you amazing things from every corner of Computer Science.
Today I want to talk about Generative Adversarial Networks 👇
🍬 But let's begin with some eye candy.
Take a look at this mind-blowing 2-minute video and, if you like it, then read on, I'll tell you a couple of things about it...
Generative Adversarial Networks (GAN) have taken by surprise the machine learning world with their uncanny ability to generate hyper-realistic examples of human faces, cars, landscapes, and a lot of other stuff, as you just saw.
A silly excuse I just invented to share with you random bits of theory from some dark corner of Computer Science and make it as beginner-friendly as possible 👇
Today I want to talk about *Algorithmic Complexity *.
To get started, take a look at the following code. How long do you think it will take to run it?
Let's make that question more precise. How long do you think it will take to run it in the worst-case scenario?
We can see that the code will run slower if:
👉 your computer is older;
👉 the array is longer; or
👉 x happens to be further to back, or not present at all.
Can we turn these insights into an actual formula? We will have to get rid of ambiguous stuff like "old computers".