I think the pattern recognition happens once the concept “clicks”.

At some point you build a machine in your brain that automates and simulates some mechanics and your role shrinks into feeding the machine with the relevant information. Then it’s easy, you can imagine what will happen just by looking at the problem.

I believe the trick is to properly understand the low level basics, explore edge cases and play with the machine as you build it.

Many people will try to learn the basics of the highest level possible, then proceed to master methods and cases without deeply understanding the subject. They cannot compete with the person doesn’t know too much but has very deep grasp on it, working the way from there.

I think a lot of this is simply just IQ. People like me can’t pass interviews easily, and it’s a fucking nightmare.

I see why this has been downvoted, but agree with the premise that it’s not just about practice (whether you want to call this IQ is up to you). As they say, a good mathematician finds analogies, whereas a great one find analogies between analogies. It’s not at all evident to me that pattern recognition is trainable to a high degree.

Pattern recognition and the ability to construct, destruct and handle abstract structures in your mind IS the essence of IQ. Just have a look at the usual tests.

What people don't like, or even question, about this concept is the correlation between IQ and "intelligence" (whatever this is).

IQ seems to be mostly a genetic trait. Test results kind of normalize in grown up humans. Childhood experience gets less and less significant with age, which points to a strong genetic influence. (According to Wikipedia).

I think a lot of people actually don't like the implication arising here, as it points in the direction that you need to be born "smart" to be "smart" — and that you can't change mostly anything about that significantly no matter what you do.

Nevertheless there seems to be a difference how fast, or even if, one can reach "full potential".

IQ is just a trait, and without the right stimulus it won't develop quickly. This seems no different to other genetic traits after all.

I'm of course not going into the question whether IQ is a good, or even adequate, measure of "intelligence" (whatever this is), as this is a can of worms… At least it's quite sure not the full picture. One can for example excel at arts or sports without a high IQ.

> It’s not at all evident to me that pattern recognition is trainable to a high degree.

Wouldn't that mean that mathematicians do not actually improve during their careers outside of things like management and publishing?

Of course a mathematician improves when gaining nowledge and understanding. The question is how fast that happens and how easily that can be applied in different contexts or how hard it is to even recognize when which fact might be relevant for solving a problem. Knowing something like conformal geometry algebra is useless if possible use cases don't become obvious unless you're just following some form of instructions by someone who does.

I wouldn't say this is a hard rule though as a fresh, unbiased mind can also lead one down an overlooked path with surprising results.

Well, there is the work of learning patterns, and then there’s the skill of matching patterns. In the mental model I have, the former yields improvements over time, while the latter probably declines with age. Of course, there’s also collaborations, strategizing (what to work on, when to call it quits, etc) that likely improves with time.

They improve at knowing mathematics, not raw pattern recognition ability.

I don't that's true at all. Studying for algorithm interviews are like memorizing integral solution formulas (if you recall from your calculus class). You have study, memorize and be ready to apply solutions. Algorithm interviews are not knowing general techniques, like for example knowing how to solve algebra equations. The reason why people have to study for algorithm interviews is because people interviewing use similar algorithm problems found in the practice sessions.

I disagree. Studying algorithms and data structures is not at all like memorizing integral solution formulas. People who are good at these types of problems generally do the same steps:

1. What is the class of problem I am dealing with? (e.g., solution uses a stack, queue, heap, etc.) 2. What is the likely complexity of the solution?

Knowing, or at least having a guess, to these two questions greatly simplifies the approach. You do the exact same thing with solving integrals (e.g., is this an integration by parts question? is it a substitution question? etc.) and the same for physics, chemistry, etc. With algorithms, once you've done enough and have a very good mental model, you will start making translations between problems. That is, given problem A, you are able to map it to problem B which is easy to solve using some technique you know.

The big issue is understanding what the problem is asking. Once you get that the pattern is easy.

I am not sure that is always true, there are plenty of problems that are easy to state, but finding the "pattern" can be out of reach. Eg Collatz conjecture? However there are obviously a subset of problems I guess that can be solved once you realise what they are asking.

Are you suggesting that the Collatz conjecture is a valid interview question?

Proving it no one has any ideas, but generating the sequence given the input is definitely an interview style question.

It is, it’s a fairly standard warmup interview problem.

https://leetcode.com/discuss/interview-question/489169/Googl...

does doing leetcode on a daily basis help with help my long term memory to have a toolkit of problems solved?

or will I just forget what I learned from leetcode? if so what can I do to retain my leetcode knowledge. I just started this week not for an interview but to actually improve my long term memory of algorithms, data structure knowledge so I don't have to google everytime I wanna implement something. and hopefully to improve my reading speed when reading code

One big problem is the breadth of problems. Most people don't work with graphs, binary trees, dynamic programming, dozens of other algorithms and data structures you don't use ever daily.