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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?

#171

Post by YetAnotherNick »

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…

> Becoming an ML/DL researcher working on novel techniques or new models will be hard without academic research experience

This is not correct for current DL research. I know many undergrad engineers who wrote papers in top conferences. Current DL is mostly about implementing ideas, running experiments, having good sense of data etc. rather than theory. It's an open secret in DL that theories are just there to please reviewers and mostly gibberish and often time plain wrong. e.g. batch norm paper, where what they theorised about it was proven not just false but completely opposite. Still batch norm is heavily used because it works.

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

#172

Post by tdeck »

frontman1988 wrote: post #76

It's probably a bit too late to get into ML. It's oversaturated with a lot of wannabe "Machine Learning enthusiasts" If you still want to get into the field a masters/phd is a much safer way to get proper ML jobs and then prosper in them.

Not sure why this is downvoted, perhaps because it's condescending. But I've observed the same thing. The data science job market is not that great right now; It's tough even if you have a master's degree.

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

#173

Post by carom »

natch wrote: post #72
urthor wrote: post #56

Earlier quoted context omitted.

Strongly disagree. There's a vast amount of work that doesn't involve unethical recommendation systems. Expand your horizon outside the Bay Area. The plurality of work I see is straightforward computer vision/NLP applications.

I suspect the work you're talking about could be easily handled by an intern working with Core ML and a MacBook. The landscape is varied. There are companies doing real actual big leading edge stuff, there are companies where ML is sprinkled onto projects as a buzzword but no real interesting work happens, and companies that just need a practical small solution like the ones you mentioned, and could get by with Core…

What does productionizing coreml look like if I wanted to stand a model up as an rpc service?

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

#174

Post by unoti »

carom wrote: post #164
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.

This is the best way to get into deep learning: Do both FastAi, and also do Deeplearning.ai. They take opposite approaches that compliment each other perfectly. Fastai starts with the practical and doubles back to help you understand the technical. So in lesson 1 of fastai you're doing practical work, and half way through the course you start learning about taking derivatives of loss functions. Deeplearning.ai on the other hand in lesson 1 you're learning about taking derivatives of loss functions, and you do practical things with it starting about half way through the course. They both are absolutely fabulous for their own reasons, but the two together really cover each other's strengths and weaknesses in an amazing way.

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

#175

Post by carom »

bsenftner wrote: post #163
badpun wrote: post #155

Earlier quoted context omitted.

> 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.

I'm confused here, does migrant mean illegal or is there some program similar to farms to bring people across the border to work?

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

#176

Post by carom »

LZ_Khan wrote: post #170
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…

Or through a free online course like deeplearning.ai or fast.ai, and a few personal projects.

So many PhDs I've worked with have known the literature but couldn't produce something valuable to save their life. That might have been the nature of them being over billed on projects and spread too thin, but I do not think that is a requisite for building models. It certainly isn't for the rest of the engineering pipeline.

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

#177

Post by LZ_Khan »

carom wrote: post #176
LZ_Khan wrote: post #170

Earlier quoted context omitted.

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…

Or through a free online course like deeplearning.ai or fast.ai, and a few personal projects. So many PhDs I've worked with have known the literature but couldn't produce something valuable to save their life. That might have been the nature of them being over billed on projects and spread too thin, but I do not think that is a requisite for building models. It certainly isn't for the rest of the engineering pipeline…

It depends on what company you're going for. If you are looking for a role in cutting-edge ML (FAANG/OpenAI/Deepmind/etc), the stuff you learn from these online courses does not provide the theoretical rigor required.

If you want a role in a small company building best-effort, out-of-the-box models, then the courses are plenty fine.

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

#178

Post by vasili111 »

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…

What about salary of pure ML vs Data/ML Engineer?

Is there difference and who gets more and how much more?

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

#179

Post by kwoff »

jvans wrote: post #147
icedchai wrote: post #121

Earlier quoted context omitted.

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

Richard McElreath's Statistical rethinking is an absolute masterpiece. https://xcelab.net/rm/statistical-rethinking/ on statistics

Thanks. BTW, I found there's a much cheaper ($80 -> $27) paperback version published a few days ago.

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

#180

Post by bsenftner »

carom wrote: post #175
bsenftner wrote: post #163

Earlier quoted context omitted.

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.

I'm confused here, does migrant mean illegal or is there some program similar to farms to bring people across the border to work?

It means both; to the employer they are good low expense labor and the business is wise to hire them, to the working class they are illegals taking jobs, (their illegal status tends to be in control of their employer, btw) to the political class they are a source of outrage funding, to the workers themselves they are simply struggling to survive anyway they can - caught by bad luck and an unforgiving world.
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