ummm you are probably wrong.
those job postings probably want people with PhDs in Computer Science/Statistics/Applied Math that did hard things with data.
for anyone who maybe got confused SLAM stands for
Simultaneous Localization and Mapping.
edit: I think if it is possible to do SLAM without LIDAR waymo will definitely beat TESLA to the punch since waymo has an absurd amount of vision data labelled with the lidar-measured-depth.
we call them hidden layers because of path dependence i.e. that is what people called them at first.
the first neural network-like things would have input nodes directly connected to output nodes. Thus both layers were 'exposed' and visible. The hiddden layers are 'hidden' because you don't directly touch them or use them-they are obscured. You can call them intermediate layers if you want I suppose-people will probably understand what you mean
1. Immediately relevant to my current interests
2. by people I who know write good papers.
For chaff-vs-wheat some of my friends use arxiv sanity?
to find popular articles I go to
-google scholar page of someone well known for a topic and look at their highly cited papers
-look at citations of highly cited papers
edit: just a warning-short term popularity of a scientific paper is almost surely not a good indicator of long term value.
100% correct. I think we are both very lucky in that we are able to do fun science and chase our intellectual interests with a realistic and still fun safety net.
Also those closet programmers are always really fun to talk to since their problems and culture are breath of fresh air.
I could rant about this for hours. I actually just went to a defense for a deep learning paper that had a ton of abstract algebra. I am honestly not really a fan of deep learning and algebra because all the papers to me like- seem to stop at describing some really basic feedforward network as some really specific mathematical structure but these theories a) provide very little explanation of empirical phenomena b) provide no new directions of research in terms of like useful network architectures.
I haven't really come across algebra in machine learning other than people applying it to deep learning.
This book is nice since it really balances theory with a more practical understanding.
-An Introduction to Computational Learning Theory by kearns is a classic [low priority]. this is fun since the proofs are simple and deep but is very very far away from practical algorithms.
-convex optimization by boyd
Course Notes:
[I think a good alternative to blogs is stalking course notes for other schools-they are very often
public.]
Well I would be happy to provide some context.
I just finished my first year of CS Phd in ML (more on the theory side) and I really like it. I think most of the places you would want to do a post doc in CS are probably going to be moderately high CoL. My phd is in a place with pretty low CoL (but a still a top 10-top 20 school (depending on who you ask) ) so the graduate stipend goes reasonably far.
The other thing to note in ML is that it seems like a few people go to industry research labs for a few years i.e MSR/FAiR/google brain and then come back to the academy since there are industry roles that involve research and publication. for instance moritz hardt.
my personal plan for the first 3 years of grad school is to work really hard and try to keep both academia and industry open and after year 3 evaluate the number of publications I have and my current skill set to see if I can make it in the academy or shift more towards industry.
I think the biggest factor I would comment on is look very closely about what jobs the graduated students from the department you matriculate at AND more importantly the professor you want to work with go on to do post Phd. There are a lot of naysayers in this thread about the risks of an academic career and I share those concerns but I felt a lot more comfortable taking the plunge after I looked at the career record of the graduated students of my advisor. They were all either tenure track or had good industry positions.
edit: if your advisor has collaborators in industry groups I think it is pretty straight forward to get an industry gig.
are you already a grad student or are you considering it?
in my department [we are an outlier probably] post doc wages are actually pretty comparable to industry i.e. >= 100k. 40k to 70k seems low to me.
I would expect the range to be more like 60-100k.
I haven't been on the real job market yet-but the key to getting a TT or postdoc position seems to be a) collaborators who want to hire you b) people who have heard you give a talk in person and are familiar with your research.
all the CS postdocs I know see a) pretty happy b) have a really easy back up of a high paying industry gig that they can pivot to because of currently insanely inflated demand for academics to make deep learning on the blockchain on the cloud on embedded devices doing quantum backprop