[mendeza]

I wish the people who answer this question are people that are current deep learning engineers or data scientist that use deep learning in real world settings, I am worried that people who are not credible are giving advice, which is not valuable. I am a masters student taking a PhD class in Bayesian machine learning to figure this out as well. I hope to have a better answer for this by the end of the course!

[mindcrime]

I wish the people who answer this question are people that are current deep learning engineers or data scientist that use deep learning in real world settings,

Why do you want answers only from people doing deep learning? Deep learning is just a subset of the overall field (albeit an incredibly popular and useful one).

Anyway, the simple solution is just to use some simple machine learning of your own to analyze the data set which these threads constitute. Look for patterns... are certain answers being repeated over and over again, by different posters? Then I'd argue that your Bayesian posterior for "this is legitimately important" should go up.

Take Linear Algebra for example... given the sheer number of people saying "linear algebra" in their answers, it seems a reasonably bet to me that LA is really, truly useful. Either that or there's some really freaking group-think shit going on. :-)

[mendeza]

I guess what I am looking for is advice from practitioners who wont lead people astray who are really interested in diving deep into ML.

I have attempted to read the Statistical Learning book, and its so daunting because the book expects a lot of background knowledge, and it takes a while to really wrap your head around these concepts. I think people should learn from a lighter book, before diving into these books if you are lacking the background.

My current approach to pursuing a career in DL and ML is going to graduate school, taking a graduate ML course, and trying to apply my knowledge to different problems I am interested in.

I am reading the Bishop book Pattern Recognition now. I think from the perspective of having to re-learn a lot of calculus and probability, that book is more approachable than Statistical learning.

My advice (which I am attempting now) to dive deep into ML is follows:

1. Taking Bayesian ML class (at Cornell) 2. Read/Study Pattern Recognition by Bishop, for 5hrs/day 3. Try exercises, if fail, review solutions 4. If lost(which is usually), review missing concepts from MIT OCW scholar courses

[xenihn]

>I wish the people who answer this question are people that are current deep learning engineers or data scientist that use deep learning in real world settings

There simply aren't very many people in those roles because the number of ML/AI/DL jobs out there are still limited, I think.