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My story as a self-taught AI researcher

blog.floydhub.com

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Re: My story as a self-taught AI researcher

#121

Post by FranzFerdiNaN »

deepnotderp wrote: post #33
K0SM0S wrote: post #29

This was a great read (and great nuggets, like that paper on Intelligence by Chollet). I wonder: — Is math a problem for non-academic researchers? Most papers strike me as requiring a non-trivial knowledge of linear algebra, for instance; and topology sits right behind; the bold seem to take it one up on category theory as we speak, and geometric algebra is quickly gaining traction too. Lots of math, cool math but ma…

To be honest, linear algebra is not that difficult to learn on your own, and plenty of people do. Gilbert Strang's course on OCW has made introductory linear algebra quite accessible. Things like topology (e.g. TDA, persistent homology, etc.) aren't really mainstream yet, but even then most of it isn't really "hardcore" math in the sense that you can get away with a basic understanding, e.g. what a Vietoris-Rips comp…

I work as a data engineer, and i was interested in learning some of the stuff our data scientists do so i can better communicate with them. Teaching myself some statistics was fine, probability was fine too and quite fun and surprising. Both subjects have plenty of books that allow you to understand the intuition behind the things they do without having to dive deep into the proofs. Linear algebra was and still is a struggle though. I've sampled many books, from Strang's book to Linear Algebra Done Wrong/Right to some books that are used in the local university in CS courses. But they are all the same. It's clear they are all written by mathematicians for math students, probably to be used as a way to teach students how to write proofs at the same time? It's just one page after another of increasingly esoteric calculations and proof after proof after proof. Which is fine if you study math, but bad for me, because i dont want to work out the proof that taking the determinant of an inverted matrix works, i want to know what it means and why one would want to make the effort to do it.

Basically, i want a book like Statistical Rethinking or Blitzstein's Introduction to Probaiblity, but for linear algebra. And i havent been able to find it.

Re: My story as a self-taught AI researcher

#122

Post by fock »

wigl wrote: post #39

This reeks of survivorship bias to me. I much prefer Andreas Madsen's more sober and self-conscious take on independent research [0]. > I’d spend 1-2 months completing Fast.ai course V3, and spend another 4-5 months completing personal projects or participating in machine learning competitions... After six months, I’d recommend doing an internship. Then you’ll be ready to take a job in industry or do consulting to se…

and truth be told: anders madsen has a full CS degree. So he basically took a 1-2 year private PhD-time, in which he produced 1 paper. Which is not terribly special. As for the person in question: there's money in it and if there are more aspiring people to do the grunt work (data cleaning, hyperparameter tuning) eventually prices will fall. And you definitely don't need a CS degree for that.

Re: My story as a self-taught AI researcher

#123

Post by burntoutfire »

FranzFerdiNaN wrote: post #121
deepnotderp wrote: post #33

Earlier quoted context omitted.

To be honest, linear algebra is not that difficult to learn on your own, and plenty of people do. Gilbert Strang's course on OCW has made introductory linear algebra quite accessible. Things like topology (e.g. TDA, persistent homology, etc.) aren't really mainstream yet, but even then most of it isn't really "hardcore" math in the sense that you can get away with a basic understanding, e.g. what a Vietoris-Rips comp…

I work as a data engineer, and i was interested in learning some of the stuff our data scientists do so i can better communicate with them. Teaching myself some statistics was fine, probability was fine too and quite fun and surprising. Both subjects have plenty of books that allow you to understand the intuition behind the things they do without having to dive deep into the proofs. Linear algebra was and still is a…

I self-taught myself linear algebra from Strang's book, combined with his lectures (on youtube). I wouldn't say that his approach is proof-heavy at all. Furthermore, the proofs in LA are rather easy and mechanical, compared to other areas of maths (and also, you can just skip them when reading).

Re: My story as a self-taught AI researcher

#124

Post by newswasboring »

I'm not going to lie, his life story made me jealous. Extremely jealous. He did all the things I wanted to do (speaking in categories, not exact things) and is free to do more. It seems like in some cultures (mine is South Asian) there is a threshold on exploration time. Usually around the age of 28-30 years old (for some even lower than that, I consider myself one of the most fortunate ones). As I approach that number I feel the invisible hand of expectations and responsibilities crushing my spirit. But I must also remember comparisons on life scales don't really work and nobody can win neither the happiness Olympics nor the misery Olympics.

Re: My story as a self-taught AI researcher

#125

Post by itsmefaz »

vector_spaces wrote: post #91

> Creating value with your knowledge is evidence of learning. I see learning as a by-product of trying to achieve an intrinsic goal, rather than an isolated activity to become educated. > Early evidence of practical knowledge often comes from usage metrics on GitHub, or reader metrics from your work blog. Progress in theoretical work starts by having researchers you consider interesting engage with your work. > Taste…

An underrated comment!

Re: My story as a self-taught AI researcher

#126

Post by itsmefaz »

The problem with Emil's approach to learning is that it restricts his ability to learn anything that he has no intrinsic goal off. That includes areas like pure mathematics, theoretical computer science, finance, economics, literature, etc. Those subjects require a different sort of motivation than a motivation to just achieve a set goal i.e capitalistic motivation

Also, Emil's approach to learning will create a flawed sense of expertise. Look at how the article presents him as if he has a deep-domain expertise which might not be true.

One important thing to consider is to look at the article more like content marketing tactic, that FloydHub is using promote its brand which might not serve well for engineers as it lacks some aspect of truth.

Re: My story as a self-taught AI researcher

#127

Post by itsmefaz »

exdsq wrote: post #14

Survivorship bias or reality: 3 months learning FastAI, 3-12 months personal projects and consulting, 2 months flashcards of ~100 papers, 6 months to publish a paper What does he mean by ‘paper’? A Medium post? NeurIPS?

Submitting papers in conferences rather than journals.

Re: My story as a self-taught AI researcher

#129

Post by Insanity »

newswasboring wrote: post #124

I'm not going to lie, his life story made me jealous. Extremely jealous. He did all the things I wanted to do (speaking in categories, not exact things) and is free to do more. It seems like in some cultures (mine is South Asian) there is a threshold on exploration time. Usually around the age of 28-30 years old (for some even lower than that, I consider myself one of the most fortunate ones). As I approach that numb…

I think the limit is mostly set by having kids, and not necessarily age. My wife and I travel frequently (if you consider this exploration time, but I might have misunderstood). And in addition she is now going back to university for a third master after having worked for some time.

We're about the age you describe, late-20s, early-30s. But our friends of around the same age with kids seem to have a lot less of this 'freedom'. Responsibilities take over.

Not saying you can't do those things with kids - but it does seem harder.

Re: My story as a self-taught AI researcher

#130

Post by ramblerman »

itsmefaz wrote: post #126

The problem with Emil's approach to learning is that it restricts his ability to learn anything that he has no intrinsic goal off. That includes areas like pure mathematics, theoretical computer science, finance, economics, literature, etc. Those subjects require a different sort of motivation than a motivation to just achieve a set goal i.e capitalistic motivation Also, Emil's approach to learning will create a flaw…

It's not a zero sum situation. Him taking this approach does not restrict someone else from studying what they want.

Also finance... really? I'm not sure I would agree that is a subject with a less capitalistic motivation.

I don't get a lot of the bitterness here. I mean not everyone here creates their own programming language, or would know how to write a database, yet we are fine using them as tools. Why must we understand all the code and concepts in a neural network to apply it?

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