Read “AI a modern approach“ and do the MIT and Stanford courses that are available in Youtube.
Then you can go deeper into the branches presented in the book and courses.
The problem I’m seeing now, ist that everybody seems to think AI/ML is NN. Nothing further from truth!
Ask HN: In 2022, what is the proper way to get into machine/deep learning?
41–50 of 211 posts
Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?
#42Earlier quoted context omitted.
> I think it can yield the highest success rate. At what? If you want to be an SRE for a data platform, sure, but this pretty thankless work: - cleaning up dodgy data - cleaning up behind low code data pipelines and other painful integration work with systems that suck and sometimes just don’t work (like PowerBI). - cleaning up behind data scientists that create models in arcane and imaginative ways and expect you to…
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.
In smaller teams/companies one gets to wear multiple hats. However, the term "Data engineer" was specifically created by/for ML folks to get rid of unpleasant repetitive work that has to be done but nobody looks forward to it.
Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?
#43Can I suggest a longer, but (I think) better route? Try the Data/ML Engineer route. Instead of going directly into ML, try to work as a “supporter” of those doing ML. There’s a HUGE gap there, specially if you’re a good programmer. There are a lot of people in the “pure” ML space, people with science background, with phDs, etc. But there’s not enough people to support them: taking their models to producing, building…
> I think it can yield the highest success rate. At what? If you want to be an SRE for a data platform, sure, but this pretty thankless work: - cleaning up dodgy data - cleaning up behind low code data pipelines and other painful integration work with systems that suck and sometimes just don’t work (like PowerBI). - cleaning up behind data scientists that create models in arcane and imaginative ways and expect you to…
SRE = Site Reliability Engineer?
Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?
#44Can I suggest a longer, but (I think) better route? Try the Data/ML Engineer route. Instead of going directly into ML, try to work as a “supporter” of those doing ML. There’s a HUGE gap there, specially if you’re a good programmer. There are a lot of people in the “pure” ML space, people with science background, with phDs, etc. But there’s not enough people to support them: taking their models to producing, building…
> I think it can yield the highest success rate. At what? If you want to be an SRE for a data platform, sure, but this pretty thankless work: - cleaning up dodgy data - cleaning up behind low code data pipelines and other painful integration work with systems that suck and sometimes just don’t work (like PowerBI). - cleaning up behind data scientists that create models in arcane and imaginative ways and expect you to…
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 names draw people in, but basically get to work on whatever they want--and you need a top-10 PhD at an absolute minimum to be eligible for those.
The ugly truth about industry is that 99.9997% of it is flow capture based on power relationships, found artifacts (i.e., corruption opportunities) within the state, and the implementation of very simple processes but in a way such that the threat to executive reputations as a first priority, and profit as an important second one, are minimized. This doesn't exactly make a market for ML innovation, unless your boss for some weird reason still cares about being a co-author on your papers (which his bosses will pressure him not to let you publish, because after all, this publishing is a distraction from your paid work).
On the other hand, if you want to be able to afford a house in the Bay Area, and to be tapped for (indeed, most likely forced into, both due to losing interest in and being unhireable for IC work) management in your mid-30s... then go for industry. The poison carrot will make you sick but it will kill you more slowly than poverty, so that ain't so bad, now is it?
Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?
#45Earlier quoted context omitted.
> I think it can yield the highest success rate. At what? If you want to be an SRE for a data platform, sure, but this pretty thankless work: - cleaning up dodgy data - cleaning up behind low code data pipelines and other painful integration work with systems that suck and sometimes just don’t work (like PowerBI). - cleaning up behind data scientists that create models in arcane and imaginative ways and expect you to…
> It’s IT support for the people doing real work. This is an appalling perspective. Good MLE skills seem a lot harder to find that good ML ones.
I think he meant "real work" from the perspective of the overeducated people who tend to end up in (and resent being in) corporate data science roles.
From the boss's perspective, the grungy IT support stuff is closer to real work (although the only thing that's actually respected in management, because it's what they do) than the shiny ML stuff that one star hire is allowed to do because it makes the company look cooler than it actually is.
Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?
#46Earlier quoted context omitted.
> I think it can yield the highest success rate. At what? If you want to be an SRE for a data platform, sure, but this pretty thankless work: - cleaning up dodgy data - cleaning up behind low code data pipelines and other painful integration work with systems that suck and sometimes just don’t work (like PowerBI). - cleaning up behind data scientists that create models in arcane and imaginative ways and expect you to…
SRE = Site Reliability Engineer?
yes
Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?
#47My 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 good suggestions on this thread, I won't chime in), and you miss the fact that, sometimes, a simple decision tree, or dimensionality reduction approaches (e.g. PCA or ICA) can yield an incredible value in a very short time on huge datasets.
I had written a fairly short post about it when I finished my georgia tech path https://www.franzoni.eu/machine-learning-a-sound-primer/
3) It can take a lot of time to become effective in ML, effective as in, what you _manually create_ is as effective as picking an existing trained model, fine tune it, and use it. This can be frustrating: low hanging fruits are pretty powerful and you don't need to understand a lot about ML algorithms to pick them up.
4) Consider MOOCs or online classes. I took Georgia Tech OMSCS, I can vouch for it and some classes force you to be a data scientist and read papers as well, and you can have "real world" recognition and discuss with your peers, which is useful!
Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?
#481. Clarify your goal.
Do you want to:
a) Become an academic in mathematics/statistics.
b) Become an academic in computer science with a focus on artificial intelligence.
c) Become a MLE in "regular" statistical applications. Aka bayesian classification, "core" statistical principles.
d) Become a specialized computer vision/natural language processing focused MLE.
e) Become a generalist software engineer who can whip out the above if needed.
In no way is e) the inferior option.
Generalists who can write code fast with 100% test coverage and pristine logging are by far the segment the industry has the shortest supply of.
There are TONS of math guys. Vanishingly few Principal Engineers who can write a design document and lead a project.
(Machine learning customers are OBSESSED with test coverage and verifiability. Believe it or not, multinational corporations generally don't want to unleash a {your_adjective_here}ist algorithm on the world.)
2. Study the above, properly.
To study the math, Elements of Statistical Learning/Algorithms by Goodfellow.
Start on page 1, do every second exercise. Publish a summary of every chapter you finish with your answers to GitHub.
3. Pursue your goal in a publicly verifiable manner.
See:
Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?
#49The dilemma is that although there are recommendations like fast.ai to get your hands dirty quickly into ML/DL, none of the good AI researchers and practitioners got there via these quick tutorials. They got there via rigorous linear algebra, traditional ML, statistics and related computer science knowledge.
I would say try fast.ai for a quick taste of what ML/DL is like, and then go back to linear algebra, deep learning and stats courses from top schools while picking a personal project goal to achieve (e.g. reproducing a popular CVPR/ICML paper results or building your own XXX) Once you go through a full lifecycle of building something from scratch, you will have a much better understanding about where you are and wanna go from there.
Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?
#50Earlier quoted context omitted.
> I think it can yield the highest success rate. At what? If you want to be an SRE for a data platform, sure, but this pretty thankless work: - cleaning up dodgy data - cleaning up behind low code data pipelines and other painful integration work with systems that suck and sometimes just don’t work (like PowerBI). - cleaning up behind data scientists that create models in arcane and imaginative ways and expect you to…
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 within 5 minutes is a pro. Kinda ruined the mystique of Star Wars, just like people jumping into ML with no training ruins the mystique of ML.