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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?

#161

Post by jedberg »

ford wrote: post #117

I work in ML - I might make 3 buckets for ML careers right now: 1. ML/DL Researcher 2. Data scientist - 20/80 engineering vs modelling 3. ML Eng - 50/50 (or 70/30) engineering vs modelling People suggesting working in engineering to support ML are right that there's a lot of demand, but it's not what you're asking for. Becoming an ML/DL researcher working on novel techniques or new models will be hard without academi…

I would add a fourth bucket -- ML Ops. Operating an ML based system is different enough from other systems that I'd consider that it's own specialty.

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

#162

Post by rjdagost »

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…

There is PhD level math involved. And yet, ML (deep learning in particular) is much more of an empirical endeavor than many would like to admit. A deep understanding of the underlying mathematics does not necessarily give you a better model. Modern models are so complicated that no one can reason through them. Parameter spaces are non-convex and fully of ugly pathologies that make neat and tidy analysis methods useless.

From one perspective, it is disheartening that a deep understanding of the underlying methods doesn't necessarily win the day. From another, it is quite remarkable that having good implementation skills and a methodical mindset can get you quite far.

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

#163

Post by bsenftner »

badpun wrote: post #155
bsenftner wrote: post #127

Earlier quoted context omitted.

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.

> We also have children working, in dangerous and toxic conditions. Can you elaborate on that? I've never heard that parcitular thing about the US. For reference, In Kongo, there are 5 year olds today carrying heavy buckets in makeshift cobalt mines, a'la XIX century England or France (plus the toxicity of cobalt, people who work in these mines get cancer if they don't die in an accident first). Even with whole famil…

Gee, the news appears to be scrubbed from most the 'net now, but I recently read about Mitsubishi using child labor in the US: https://flipboard.com/article/major-car-company-used-child-l... This is not as bad as your reference, but know where our police do not go anything is on the table. The US plays extreme.

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

#164

Post by carom »

bluelightning2k wrote: post #14

FastAI. Specifically "Deep Learning for Coders" which was recently updated. https://course.fast.ai/ Do what the instructor recommends: watch each lesson once in its entirety and then re-watch it while playing along. But don't just type their commands verbatim. Try and do something slightly different.

For the bottom up side of this, https://deeplearning.ai. Where fast.ai gets you started training models immediately, deeplearning.ai has you implementing neural nets from scratch in NumPy then transitions to TensorFlow.

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

#165

Post by solardev »

jedberg wrote: post #161
ford wrote: post #117

I work in ML - I might make 3 buckets for ML careers right now: 1. ML/DL Researcher 2. Data scientist - 20/80 engineering vs modelling 3. ML Eng - 50/50 (or 70/30) engineering vs modelling People suggesting working in engineering to support ML are right that there's a lot of demand, but it's not what you're asking for. Becoming an ML/DL researcher working on novel techniques or new models will be hard without academi…

I would add a fourth bucket -- ML Ops. Operating an ML based system is different enough from other systems that I'd consider that it's own specialty.

Could you please provide some examples of what a ML Ops person might do/manage in their day to day jobs?

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

#166

Post by tunesmith »

I've thought about going in this direction multiple times, but I have this impression that it's pretty much unavoidable that you'd be working with annoying ETL processes and "cleaning data" (parsing, transforming from one set of columns to another, etc) over and over again. Is this true? I hate that stuff, but I'm particularly interested in graphical data structures and I even like probability and statistics and linear algebra, so I've been torn.

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

#167

Post by jedberg »

solardev wrote: post #165
jedberg wrote: post #161

Earlier quoted context omitted.

I would add a fourth bucket -- ML Ops. Operating an ML based system is different enough from other systems that I'd consider that it's own specialty.

Could you please provide some examples of what a ML Ops person might do/manage in their day to day jobs?

- Tracking changes is different. Not only do you have to track code changes, but also training data and model changes. You need to build systems that allow for this change management.

- Monitoring -- you have to build specialized monitoring to check for model degradations. Is the model still outputting valid/correct predictions? If not you need to roll back to an old model (see above).

Those are the two main ones.

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

#168

Post by solardev »

jedberg wrote: post #167
solardev wrote: post #165

Earlier quoted context omitted.

Could you please provide some examples of what a ML Ops person might do/manage in their day to day jobs?

- Tracking changes is different. Not only do you have to track code changes, but also training data and model changes. You need to build systems that allow for this change management. - Monitoring -- you have to build specialized monitoring to check for model degradations. Is the model still outputting valid/correct predictions? If not you need to roll back to an old model (see above). Those are the two main ones.

Oh wow, those are actually fascinating problems! Thank you.

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

#169

Post by time_to_smile »

psi75 wrote: post #71
bitL wrote: post #36

Earlier quoted context omitted.

Data Engineer is the outsourced part of what no ML researcher wants to do - a thankless, high-pressure, dead-end job which in no way leads to actually doing ML later - it would pigeon-hole the OP as unfit for real ML. The best way is to take Stanford Deep Learning courses at SCPD, build a reputation, do real ML work (even if it's not a PhD, it's the same courses Stanford PhDs take).

I agree with your first sentence. I'm not sure I would recommend SCPD. If you want to do real ML work, you pretty much need the PhD. This is a hard thing for people who have 140+ IQs but do poorly for whatever reason with formal education to accept, but it's true. Even if you get one real ML job without a doctoral degree, you won't get a second one. Sure, other 140+ IQs can recognize very smart people with only (or n…

I've worked in industry doing what I would consider "real ML" (i.e. shipping models that are core, revenue generating product features into production) for a decade at a range of companies from startups to fortune 500 companies and the part about needing a PhD is entirely nonsense (as is the majority of the content in this comment).

Maybe if you consider doing "real ML" exclusively working with Deep Mind or on Meta core research team, but there's a lot more to "real ML" than just these teams.

I'm curious how long you've been in industry and what types of orgs you've worked at to get this impression?

edit: In general the comments in this post are bizarrely out of touch with reality and have really shifted my perception of the avg HN commenter.

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

#170

Post by LZ_Khan »

santiagobasulto wrote: post #22

Can 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…

As a Data/ML Engineer, I cannot quite recommend this route: this role is unlikely to get you to a true ML role.

It will give you a ton of exposure to ML techniques and infrastructure practices, but the true modeling work is still done by PhDs with the prerequisite background/knowledge. You will be taking black boxes -- pre-built models -- and doing the data cleaning, fine-tuning, and experimentation.

I know a handful of people who transitioned to full-on ML from this role, but those individuals were quite gifted and hardworking, and probably could have learned without this exposure. There's a significant gap in knowledge that can only be attained through academic experience.

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