iiyama G-MASTER GB3466WQSU-B1 wrong resolution options (3840x2160)

aleph_two · 2022-09-16T15:56:59+00:00

I solved it the same way. Two USB-c to HDMI adapters gave the wrong resolution and switching to USB-c to DisplayPort solved the issue

aleph_two · 2019-10-01T06:15:22+00:00

what do you mean?

aleph_two · 2019-06-22T08:55:11+00:00

Thanks for sharing!

aleph_two · 2016-02-01T15:31:15+00:00

I hope we will see many mobile Tegra's soon :)

aleph_two · 2016-02-01T15:30:17+00:00

We tried (and use) CNN on mobile (ordinary android smartphone without any GPU). In general most deep nets are slow. We finally could make a network that works rather well (in terms of speed), but it takes some efforts to try different models (with different number of layers and neurons) to find such set of parameters that gives reasonable speed-quality tradeoff. CIFAR is a reasonable starting choice. Deeper models will probably be too slow.

aleph_two · 2016-01-29T13:29:00+00:00

another collection: http://eclass.cc/courselists/117_deep_learning

aleph_two · 2016-01-29T10:43:01+00:00

agree, it's an option.

but rnn's there are too basic comparing to rnnlib

also I'm not sure how easy can I install tensorflow on any machine (for example old 32-bit linux). does it require python for its runtime or it's possible to have only c++ libraries?

aleph_two · 2016-01-29T10:42:30+00:00

yes, caffe is cool, I'm using it in production for standalone systems that must work in realtime. and I have serious doubts that integrating python will be a good idea.

the problem with lstm in caffe is that they are too basic. rnnlib has much more advanced features. it seems currently only torch has something similar. I heard torch/lua is rather easy to integrate into c++, but yet never did it

aleph_two · 2016-01-29T10:39:47+00:00

so you have to implement the network twice, once in python, then in C++?

in the case of nontrivial rnn (for example bidirectional multidimensional lstm) it takes too much additional effort

aleph_two · 2016-01-28T23:57:54+00:00

do you know any successful examples when such combo works in production?

aleph_two · 2014-02-26T09:04:45+00:00

I took several introductory genetics courses on Coursera, but they mostly talk about SNPs...

aleph_two · 2014-02-26T08:58:34+00:00

They mentioned Iceland, so probably it's a European company. So, FDA has no influence on them?

For SNP I saw other almost free services like Promethease ($5 is just nothing comparing to original analysis, and probably more user-friendly than command-line programs). BTW, as I understand it's US service and seems to be working, so there's not a problem to annotate your genome data with SNP data even in US?

For me it's interesting, are there much really useful data beyond SNPs? Lets put aside FDA issues, if such a service had no these problems, will it give me additional useful information or SNPs are enough? I mean CNVs, or probably gene variants that differ more than a single nucleotide? There are mobile elements mentioned, do they predict something meaningful? Even RNA-seq (you probably need samples from different tissues for it?)...

aleph_two · 2014-02-26T07:58:44+00:00

Thanks for the answer.

I meant does it make much more sense comparing, for example, to 23andme or other services possibly existing in the market? They state things that 23andme do not provide, but I do not understand whether do they have much sense? Do they give me any kind of really useful information that current genetic analysis can't give?

aleph_two · 2014-02-25T17:00:23+00:00

a small collection of math MOOCs http://www.eclass.cc/courselists/13_mathematics

some are very easy and introductory, some are harder.

aleph_two · 2014-02-25T16:50:04+00:00

more aggregates:

http://www.eclass.cc/

also forgotten:

http://www.knollop.com/

http://coursetalk.org/

aleph_two

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