jph00

63 ms·

721.
by jph00 8y ago
Jeremy from fast.ai here. I've posted a summary of the course materials for anyone who's interested : http://www.fast.ai/2018/05/07/part2-launch/ Let me know if you have any questions about the
722.
Volta Tensor Core GPU Achieves New AI Performance Milestones(devblogs.nvidia.com)
1 points by jph00 8y ago|0 comments
723.
by jph00 8y ago
Jeremy from fast.ai here - let me know if you have any questions about the new course. :)
724.
by jph00 8y ago
Jeremy from fast.ai here. I think it's tough to really substantiate this claim fully in a mainstream tech publication, without getting into details that are likely to be rather dull for much of the audience. However, I do think that
725.
Cutting Edge Deep Learning for Coders: 2018 Edition(fast.ai)
23 points by jph00 8y ago|1 comments
726.
by jph00 8y ago
Based on Smith's paper and our experiments, it seems pretty important. But there's still much research to be done to understand momentum tuning and related concepts.
727.
by jph00 8y ago
Yes data augmentation is critical
728.
by jph00 8y ago
Yes it's linked from the competition web site. It'll be integrated into the fastai library in the coming weeks.
729.
by jph00 8y ago
No you can't use pre-trained models in this competition.
730.
by jph00 8y ago
No I haven't tried yet. One of our students has started experimenting with language models (AWD LSTM) and getting encouraging results (about 3x speed improvement), but not with attention. I really hope that these results will help enco
731.
by jph00 8y ago
You can use the approach we used to win the CIFAR 10 training cost section of the competition. If you use fastai/pytorch, then it's ~5 lines of code. Check out lesson 1 of http://course.fast.ai for the basic approach,
732.
by jph00 8y ago
Jeremy from fast.ai here. Let me know if you have any questions about the methods we used, or anything else relevant to this project.
733.
Findings from the Imagenet and CIFAR10 competitions(fast.ai)
85 points by jph00 8y ago|17 comments
734.
by jph00 9y ago
Iverson explained the reasoning behind this in an appendix to a book he wrote in the 60's: http://www.jsoftware.com/papers/EvalOrder.htm It's the approach used in APL and J, and I've certainly found it f
735.
by jph00 9y ago
This is one of my favorite papers and a bigger influence on my coding than anything else I've read. It's hard to really take advantage of these ideas without a language designed for that, but I have made an attempt to document a s
736.
Beware Default Random Forest Importances(parrt.cs.usfca.edu)
5 points by jph00 9y ago|1 comments
737.
by jph00 9y ago
To clarify the grandparent comment : pytorch also supports a functional approach rather than a computational graph. There's even a pytorch functional model zoo :)
738.
by jph00 9y ago
The first 5 chapters of the Goodfellow deep learning book are a great resource for understanding the probability, linear algebra, optimization, and information theory you need to digest deep learning papers.
739.
by jph00 9y ago
The deep learning libraries largely hide all the calculus - it's all automated.
740.
by jph00 9y ago
Oh in that case I'm just confused - that's what I know as Einstein notation. Modern physics papers seemed to use much more complex notation, but I probably just misunderstood. If we're talking about Einstein notation, then I&
741.
by jph00 9y ago
You might prefer the approach at http://course.fast.ai - all the concepts are taught with code, instead of math, and understanding is developed by running experiments.
742.
by jph00 9y ago
Probably better asking that at http://forums.fast.ai , since a lot of participants there have set up local meetups, and some of them have gotten pretty big. I haven't created a meetup myself - my focus has been on the onl
743.
by jph00 9y ago
Well... this paper is really designed to be accessible with just high school math, if you take your time (a few weeks or months) and follow the references. Any time it relies on some concept, it includes a link to learning more about that c
744.
by jph00 9y ago
I don't find it very accessible, myself - but I'm not a physicist, so the materials using or about that notation aren't aimed at me. The only tensor notation I've been happy with is that used in J ( http://www.
745.
by jph00 9y ago
Thanks for the advice!
746.
by jph00 9y ago
Jeremy here. Here to answer any questions or comments that you have. But more importantly - I need to mention that Terence Parr did nearly all the work on this. He shared my passion for making something that anyone could read on any device
747.
Bookish – math-infested markdown to HTML and latex(github.com)
2 points by jph00 9y ago|0 comments
748.
Matrix Calculus for Deep Learning(parrt.cs.usfca.edu)
573 points by jph00 9y ago|81 comments
749.
by jph00 9y ago
I do, but at the moment the course is the documentation, on the whole - so it won't really be standalone until we're written proper docs! (There's basic docstrings for most functions, but they don't tell you how it all f
750.
by jph00 9y ago
Yup that should be fine. There's resources on http://forums.fast.ai to help you get set up. (I'd still suggest doing the first few lessons on Paperspace so you can focus on the deep learning, rather than the setup. It&

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