Thanks!
I like "Machine Learning: A Probabilistic Perspective" by Kevin Murphy more, but this really is a matter of taste; both are excellent.
Readit Newsgenericpseudo commented on Organized Resources for Deep Learning Researchers and Developers github.com/astorfi/Deep-L... · Posted by u/irsina
So far I have taken one introductory class on AI in general, but it did not cover machine learning. I took one class on machine learning, but I only grasped the basics of several algorithms from the class. These classes, and the textbook I've partially completed, are the extent of my knowledge.
Thank you for that suggestion.
Take as much probability and linear algebra as you can conveniently do – as much for the intuition as for the symbol-manipulation mechanics – and don't underrate the importance of domain expertise in any problem you get interested in!
The Shipping Forecast is a very significant cultural reference in the UK. It crops up all over the place. Britain is _fundamentally_ an island and a seafaring nation, and that's something Americans miss; you're never more than seventy miles from the sea. It's as iconic as, I don't know, Thanksgiving football in the US; it's a thing everyone knows about without explanation.
This is from an album which sold over 1.2m copies in the UK; one of the biggest records of the 90s: https://www.youtube.com/watch?v=SD8gO8TAr4s
>even remotely useful in these fields in four years
...Condensed Matter
I just looked up the Cambridge physics tripos and you do condensed matter in years 3 and 4. You can learn a lot in 4 years. Admittedly it's a specialisation in a general science degree.
Density functional theory, for example, is taught at undergraduate level as part of both chemistry and physics triposes. Probably materials science too, and it certainly used to be an option within earth sciences (as part of the mineral physics path).
genericpseudo commented on Apple Hires Google’s A.I. Chief nytimes.com/2018/04/03/bu... · Posted by u/ninkendo
Did Genius use AI? I thought it was more of a collaborative filtering feature where it used other people's playlists to build relationships between songs.
Collaborative filtering is machine learning, so if you consider AI machine learning, definitely yes.
genericpseudo commented on Semantic Image Segmentation with DeepLab in Tensorflow research.googleblog.com/2... · Posted by u/EvgeniyZh
Yes for Mask-RCNN. For FCN, there is R-FCN.
Overall I'm really happy to work in a domain where people share their code and models in such an open way. I take issue with detectron in particular though, because a company the size of facebook in the year of 2018 has no excuse to publish a major software package in python 2. The oldest models they implement are from 2015 (excluding VGG16 which is so prolific it's available in literally every library as python 3) and caffe2 is quite a bit more recent than that. Like I said. No excuse...
The team behind Detectron have published an enormous amount of really good research, but the Detectron codebase struck me as "good research code" rather than something you'd ideally want in production.
genericpseudo commented on Semantic Image Segmentation with DeepLab in Tensorflow research.googleblog.com/2... · Posted by u/EvgeniyZh
Is this fast enough to be used as a background removal in live streams?
Not at the kind of resolution you'd want to be using on, e.g., Twitch. In that setting, you could just use chromakey, though? That's '70s technology, cheap and very reliable.
genericpseudo commented on Semantic Image Segmentation with DeepLab in Tensorflow research.googleblog.com/2... · Posted by u/EvgeniyZh
If you're interested in this but have no background, the best place to start is "Fully Convolutional Networks for Semantic Segmentation" – https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn...
This is a very active field of research. Another thread worth pulling on is Mask R-CNN: https://arxiv.org/abs/1703.06870
It's not quite as simple as "this one has highest mAP, let's use it"; the tradeoffs are complex. In particular, as you can see in the image here, one thing DeepLab doesn't do is segment instances – so you get a mask of "people", not a mask per person. Mask R-CNN does a better job on that by design, because it predicts both bounding boxes and a mask per bounding box.
I currently work at an e-commerce company and there has been a ton of debate around how "algorithmically driven" Stitch Fix actually is. The general feel in the space from non-technologists is that computers cannot do this job well now and won't be able to do it well in the near future. Stitch Fix makes it a major brand point that computers are an important part of the process. So the real question is - are they making this point because it's true or because it helps their valuation (tech co. 5-10x multiples instead of ecomm 1-4x multiples).
The way Stitch Fix talks about it in their S-1 makes it seem like the latter is the priority. I'm not yet convinced that the practical value driven by algorithms at Stitch Fix is up to par with how much they talk about it.
I was interested in growth to understand both their growth rate but also to get a feel if it was driven by increasing user acquisition costs like Groupon, Blue Apron, etc or if it was organic.
> Stitch Fix makes it a major brand point that computers are an important part of the process
(which even if you disregard everything else is a really good line to take if you're after recruiting good machine learning engineers!)
genericpseudo commented on MPEG: A crisis, the causes and a solution blog.chiariglione.org/201... · Posted by u/clouddrover
> AOM will certainly give much needed stability to the video codec market but this will come at the cost of reduced if not entirely halted technical progress. There will simply be no incentive for a company to develop new video compression technologies
I don't think is correct. The driver for innovation in video compression technologies will continue to be the practical problems of encoding and distributing video. The quest for smaller video file sizes while maintaining quality is what has driven the development of VP8 to VP9 and now to AV1. Smaller video file sizes directly help reduce the costs of companies like Google, Amazon, Netflix, Hulu, Apple, etc. and I don't see that changing any time soon. There will be an AV2 eventually.
This means that codec development makes no sense for anyone who doesn't own either a large distribution platform or a large playback platform. (Much of the research has been done by middleware companies attempting to tax the two; their business goes from royalties to work-for-hire, at best, which is way less attractive for them).
This is fine – in a macroeconomic sense – but of course it sucks if you're one of the companies being disrupted.