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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?

#201

Post by jokoon »

ummonk wrote: post #111
jokoon wrote: post #110

I recently asked reddit how to label images by downloading a pre trained network that used image net, I got no answers. I don't know how long it would take to train such network with a cheap laptop. There are tutorials, but I don't see any cookie cutter thing. I thought there would be demos for this, since image labeling is an old problem.

FastAI has ready-to-run code that does just this. They seem to have an ImageNet package https://github.com/fastai/imagenette

this link seems to be a ML competition, not trained datasets.

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

#202

Post by YetAnotherNick »

ford wrote: post #185
YetAnotherNick wrote: post #171

Earlier quoted context omitted.

> 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…

I didn't mean to imply it needed to be _graduate_ research - however it would be news to me if they were publishing at top conferences independently of a lab or research branch at a company. How do the people you know go about it?

I didn't said they were publishing independently, just they did not had any academic research experience. e.g. in FAANG, it's not hard to get into teams which publishes ML papers. Also, I know one guy who contacted one professor, and they collaborated and published few papers while doing full time engineering job.

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

#203

Post by lmeyerov »

vasili111 wrote: post #198
lmeyerov wrote: post #192

Earlier quoted context omitted.

It's useful to work backwards from the knowledge a DS needs to be worth their weight. Imagine a small team of $400K/yr DS + $400K/yr DE + ... and whatever hw/sw . So say a $2-3M/yr project driving $3M+ of new growing revenue or $6-12M of annual savings. At bigger companies, even more magnitudes & pressure :) The DS will likely: - be close to the business case & business stakeholders to ask questions a normal lead can…

In general, does the DE have higher salary than DS? Am I understood correctly that there is much more demand for DE than for DS?

I wish I had real numbers. So instinct from what I've seen:

- a data analyst role rebranded as a DS role will be lower paid than a DE role, maybe 50% diff

- an actual DS role is probably higher paid than a DE role, but really depends on the job+co

- a great DS role and a great DE role are both super well compensated. Though maybe again DS higher than DE in most just b/c ability to more directly drive $. Unless something like an infra company, the DS will be inherently closer to the business & outcomes. ("I did this clever thing that netted 2% revenue spike that adds up to $40M/yr in new revenue, what did you do?")

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

#204

Post by kwoff »

kwoff wrote: post #194
ford wrote: post #187

Earlier quoted context omitted.

Can you share the link?

I just searched amazon for the title plus "paperback". Now that I look at it again, it says "by MAN (Author)" whereas the hard cover is "by Richard McElreath (Author)", so it's looking possibly scammy to me now, so caveat emptor...

I received the book today. I'm still not sure that it isn't a scam... The back cover is empty; otherwise, the quality is fine. I don't know if this is associated with Richard McElreath. Good luck to everyone.

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

#205

Post by sitkack »

ralphmelish wrote: post #77
urthor wrote: post #48

1. 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…

The Elements of Statistical Learning is by Hastie et al, not by Goodfellow. Goodfellow wrote Deep Learning. They are both available for free on their websites.

https://hastie.su.domains/ElemStatLearn/

https://www.deeplearningbook.org/

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

#206

Post by sitkack »

wanderingmind wrote: post #70

If you are looking for machine learning outside of Deep Learning, there are just 2 books 1. Elements of Statistical Learning (very frequentist treatment) by Hastie et.all [1] 2. Pattern Recognition and Machine Learning by Bishop(for a Bayesian treatment)[2] Both are freely available online. Reading one book will get you to top 5% practitioners and reading both will get you to top 1% [1] https://hastie.su.domains/Pape…

That [1] is a link to the uncorrected 2009 copy of ESL. The main page provides links to the most up to date version.

https://hastie.su.domains/ElemStatLearn/

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

#207

Post by sitkack »

plonk wrote: post #197
wanderingmind wrote: post #70

If you are looking for machine learning outside of Deep Learning, there are just 2 books 1. Elements of Statistical Learning (very frequentist treatment) by Hastie et.all [1] 2. Pattern Recognition and Machine Learning by Bishop(for a Bayesian treatment)[2] Both are freely available online. Reading one book will get you to top 5% practitioners and reading both will get you to top 1% [1] https://hastie.su.domains/Pape…

Machine Learning: a Probabilistic Perspective by Murphy may be a better reference. Murphy has more up-to-date books. > Both are freely available online. Reading one book will get you to top 5% practitioners and reading both will get you to top 1% At which percentage do you start meeting math PhDs from top schools? You'll most probably never meet their level of understanding just by reading books or doing exercises. H…

https://probml.github.io/pml-book/

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

#209

Post by lglm »

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…

As far as MOOCs are concerned there are many great courses. I compiled a list on https://courseskipper.com/best-machine-learning-courses-for-... and suggest that you just read descriptions and see if anything seems like a good starting point for you

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

#210

Post by robertlagrant »

wokwokwok wrote: post #35
robertlagrant wrote: post #32

Earlier quoted context omitted.

> 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.

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…

Sure - fair clarification. But conversely, you could make some awesome automation that you rule with a light touch as an engineer, or as a DS you could come up with that ML that Amazon has to recommend you endless TVs as soon as you bought a TV :)
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