Where do Machine Learning people congregate? I do not see a lot of them here on Jow Forums

Thanks user. Know almost nothing about Scala. Is data engineering just the job title of people who spend thir days cleaning data?

thanks for the linkage desu

The definitions are fuzzy and there is a lot of overlap in real life, but generally
>data science
You have a blob of data but no idea what to do with it, you develop models and prototypes and explore what you can do with the data.
The people who get to do this are MS/PhD level math/stats/CS and the barrier to entry is math/stats knowledge, not programming knowledge.
>data engineering
You design and develop the production systems that implement those models and systems into a viable product. You optimize it and all that shit.
With experience and at a smaller company you can move into a more "data science" role if you don't have an MS/PhD in stats.

Backbone of "data engineering" as I've defined it is Hadoop shit, which is all Scala/Java. There is some Python/R too so keep learning that. Hence, I recommended learning Scala because "data engineering" -> "data science" is the likely path of your career without a PhD in stats.

They hang out on and

Okay, what aspects of Scala should an aspiring data engineer focus on? And what exactly is meant by production systems in this context exactly? What's the role of C++ in all of this? Irrelevant? Thanks again.

classy bars and clubs in San Francisco and NYC

they usually have great talks about ML over a bottle of Remy Martin Louis XIII while eating caviar and snorting cocaine off of naked asian 18 year olds' bosoms

ML is just statistics there is nothing special about it.
Most of time all you ever do is research data and visualize a.k.a make graphs, plot etc to tell what is what to your client.

I'm a statistician so I agree with you on that.
I only said that cause like 90% of Jow Forums usually doesn't care/know about how ML works in its core, they just wanna get some results, accuracy be damned.

what methods do you use to gain insight from your data? Obviously it depends on the task, but is there a routine you normally go through to visualize your data, or do you just dick around until something meaningful pops up?

The ML community speaks through arXiv papers and snail mails memes to Schmidhuber when they get lonely.

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Where does anyone hang out, honestly? I watch a lot lectures on youtube and they're always talking about the "community" and what was controversial among "the community" and what "the community" eventually decided upon. Who? Where?

Don’t buy that bundle, those books are easily accessible on google, for Free.
I torrent, but there are other places to download

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It might help you get in the door but you definitely don't need MS/PhD level knowledge to do data science. It would seem like overkill to me desu.

YMMV but a lot of the work is just plotting, sanity checking and cleaning garbage from data. If you have a solid grasp of linear algebra and calculus (like undergrad math or physics major level) then you should be be able to learn the fancier stuff like deep learning. Building intuition is a different story, but you get that from experience. Although honestly I feel like a simple linear fit ends up being better than the meme shit more than half the time

I recommend getting fluent in R for interactive work (ggplot/dplyr/data.table) so you can quickly inspect data and make plots and stuff. Python might work for this too.

I dunno about ML specifically but both of my parents are research focused biologists so I have some sense of what "the community" means in academic disciplines. for starters sub-fields tend to be pretty small, enough so that you can reasonably know every major player in the field at least by reputation. for example HBV, the virus my dad studies, has maybe 2-300 labs worldwide that work with it at any reasonable level of sophistication and that's a much smaller number for labs that study it as a primary focus. unless they work at the same university these people rarely see each other face to face except during conferences but they keep abreast of what others are doing by reading the literature about their sub-field and collaborations between labs. so when someone says that a community "decided" something it usually means that a particular theory became popular/widely accepted enough that very little research, and no respectable research, is being published that seriously contradicts that claim. this of course only really holds true for university based research; if ML research is primarily conducted in industry i have no idea what the "community" would be like.

r/machinelearning
they're pretty shit desu
they mock Siraj Rival (person of color) openly in one thread
then next thread is about diversity and inclusivity
followed by another thread where they talk about their hot conference NIPS
followed by another thread where they are slamming some dude on twitter for calling their "community" toxic
it's like they're a projection of their own poorly trained markov bots
wildly swinging between calling people intolerant and then naming their conferences with acronyms that are racial slurs

datatau?

datatau.com/

why would you do that?
why would you post something that isn't ruined already in a research thread?
delet this