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Ask HN: In 2022, what is the proper way to get into machine/deep learning?

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Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#121

Post by icedchai »

alanfranz wrote: post #47

My 2c (not exhaustive for what you want to do, probably): 1) Get some statistics/probability basics. It's full of people (you can see a lot of analyses on Kaggle) that "do machine learning" but make very silly mistakes (e.g. turn categorical data into a float and use it as a continuous variable when training a model). 2) take a look at traditional machine learning approaches. Nowadays you're swamped by DL (a lot of g…

Can you recommend a good basic stats/probability course? The last one I took was roughly in 1997 ;)

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#122

Post by bsenftner »

badpun wrote: post #114
psi75 wrote: post #101

Earlier quoted context omitted.

Unfortunately, the only way to prevent hierarchy is to create a limited hierarchy (this is the purpose of constitutions) a priori; hierarchically naive organizations fail on this account. External parties will demand hierarchy simply because they want to know your organization (or nation) isn't wasting their time--no one wants to deliver a sales pitch to people who can't authorize purchases. If they're not careful, a…

> If the US falls in the next 20 years, it won't be due to Covid or Trump or nation-level adversaries; it'll be due to the obscene power given to employers, who can literally ruin an employee's life--not just fire him, but anally ravage him in perpetuity with bad references--for any reason or none. How do you define "US falls"? >Eventually, unless national governments start dropping serious lead pipe on employers' he…

> People endured much worse in medieval times, and endure much worse right now in China.

Really? The USA has hollowed out portions of the country equal/worse than the worst 3rd world countries.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#123

Post by jokethrowaway »

listenallyall wrote: post #50
LeetHacks wrote: post #34

Earlier quoted context omitted.

It highly depends. I was hired for a small research group that didn't have a product in production. Got hired for programming, was on the table discussing and contributing to research within a couple months without any background in ML.

These types of anecdotes make the actual practice of both ML and AI seem rather, well, less than scientific. There is supposed to be Ph.D. level math behind all of this, yet an amateur with admittedly no ML background is part of the team. In Star Wars , it takes Luke Skywalker years to learn to use a light saber skillfully. Then in The Force Awakens , some ex-Stormtrooper with no training picks up the light saber and…

You're disillusioned if you think a PhD is what makes the difference.

Smart people will be able to contribute even if they don't have a PhD. Some PhD are useless and everyone is wondering how the hell they go through that.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#124

Post by data_maan »

lmeyerov wrote: post #93
wokwokwok wrote: post #35

Earlier quoted context omitted.

Someone who can engineer infrastructure, pipelines and fire fight production issues is hard to find, but that’s not the point I was making. My apologies; It is real work; the point I was making is it’s not ML work , any more than writing a yaml file is ML work. If you want to write yaml files, any number of possibilities exist. If you want to work with machine learning, then don’t become a data engineer. The skills a…

What? A lot of the hard part isn't the model, and especially in a world where bert, xgboost, optuna, pytorch, etc have solved much of the classic problem and forced 'real' DS to specialize on either the business consulting side (not math/engineering) or theory side (barely implemented). The rebrand of 'data analyst' (SQL, powerbi, . ..) to 'data scientist' by even top tech companies underscores this. It's not yet to…

NeurIPS paper, not neuroips paper

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#125

Post by badpun »

bsenftner wrote: post #122
badpun wrote: post #114

Earlier quoted context omitted.

> If the US falls in the next 20 years, it won't be due to Covid or Trump or nation-level adversaries; it'll be due to the obscene power given to employers, who can literally ruin an employee's life--not just fire him, but anally ravage him in perpetuity with bad references--for any reason or none. How do you define "US falls"? >Eventually, unless national governments start dropping serious lead pipe on employers' he…

> People endured much worse in medieval times, and endure much worse right now in China. Really? The USA has hollowed out portions of the country equal/worse than the worst 3rd world countries.

I hope you're hyperbolic, the worst 3rd world countries have no governance (unless you count local warlords), 5 year olds working in dangerous and toxic conditions, hunger and slavery.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#126

Post by jokethrowaway »

mrcartmeneses wrote: post #61
psi75 wrote: post #44

Earlier quoted context omitted.

This, 100%. That said, most data scientists don't do what you would consider real work (meaning, I assume, interesting work with significant mathematical/analytical meat). There just isn't a lot that's both interesting and useful to private-sector rent-seekers whose opinions of your work determine whether or not you advance. Most of the people doing real ML in industry are prestige hires--they're hired because their…

I’ve kinda developed the view that large organisations come to mirror the Russian Communist Party. I’m interested in “flow capture based on power relationships”. Do you have any recommended reading on this?

Every large organisation tends to be like a small government. Inefficient, drown in politics and unable to change.

There are exceptions - where someone principled dictator impose a VC style model where teams basically become independent startup and die or succeed. 100 fails, one becomes the next revenue maker for the company. That's how AWS was born.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#127

Post by bsenftner »

badpun wrote: post #125
bsenftner wrote: post #122

Earlier quoted context omitted.

> People endured much worse in medieval times, and endure much worse right now in China. Really? The USA has hollowed out portions of the country equal/worse than the worst 3rd world countries.

I hope you're hyperbolic, the worst 3rd world countries have no governance (unless you count local warlords), 5 year olds working in dangerous and toxic conditions, hunger and slavery.

You don't realize what is going on in the United States. We have portions of the USA where the police don't even bother, and are run by local gangs. We also have children working, in dangerous and toxic conditions. We also have hunger, and yes we have slavery: prison labor. The USA is not what you think it is.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#128

Post by data_maan »

listenallyall wrote: post #50
LeetHacks wrote: post #34

Earlier quoted context omitted.

It highly depends. I was hired for a small research group that didn't have a product in production. Got hired for programming, was on the table discussing and contributing to research within a couple months without any background in ML.

These types of anecdotes make the actual practice of both ML and AI seem rather, well, less than scientific. There is supposed to be Ph.D. level math behind all of this, yet an amateur with admittedly no ML background is part of the team. In Star Wars , it takes Luke Skywalker years to learn to use a light saber skillfully. Then in The Force Awakens , some ex-Stormtrooper with no training picks up the light saber and…

Let me expound on this, as there's a lot of PhD hate in the comments parallel to mine.

What is unique to a PhD is that you took a very long time to master a small slice of the knowledge pie. The emphasis here is on long time: Most people simply aren't willing to go for years on a low salary and tedious job.

It doesn't mean PhD's are more (or less) creative, top coders and whatnot; it means we took the time to read all the papers, to know all previous solution attempts and who all the big players in the field are ("all" w.r.t. our niche). It also mean we can read papers much faster than other people because that is basically what we do all day.

There you have it! Now don't send me ML job offers, cause I gotta read this next obscure paper to figure out if they are legit. :D Just kidding.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#129

Post by data_maan »

1. The theory by now is filling bookshelves, so forget learning about "the theory". Learn the foundation (to see where you're niche --e.g. optimization for fully connected NNs; last I heard it should be done by second-order methods if you have the computing power-- fits in) and then learn the niche's theory.

This will require at least upper undergraduate level math BTW.

2. You could get by knowing the theory in a handwavy way. Not ideal but I've seen people do it. For implementation that is enough in many case.

3. Again, "research" is too general. While you might understand some experimental ICML papers, it's very unlikely you will understand a single COLT paper if you don't know a lot of math.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#130

Post by data_maan »

spywaregorilla wrote: post #112
alanfranz wrote: post #47

My 2c (not exhaustive for what you want to do, probably): 1) Get some statistics/probability basics. It's full of people (you can see a lot of analyses on Kaggle) that "do machine learning" but make very silly mistakes (e.g. turn categorical data into a float and use it as a continuous variable when training a model). 2) take a look at traditional machine learning approaches. Nowadays you're swamped by DL (a lot of g…

While stats and probability are very good, I can't say you need more than a good 101 level course for either. Really you're just looking for some good reasoning skills about how distributions and probability works. This example: > It's full of people (you can see a lot of analyses on Kaggle) that "do machine learning" but make very silly mistakes (e.g. turn categorical data into a float and use it as a continuous var…

Depends on your goal. If you want to read/implement things from COLT article a 101 stats/probability won't really cut it.

Though for applied papers that's sometimes enough.

But heed Larry Wasserman's advice: "Using fancy tools like neural nets, boosting, and support vector machines without understanding basic statistics is like doing brain surgery before knowing how to use a band-aid."

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