mrdrozdov

9 ms·

31.
by mrdrozdov 9y ago
This article is outdated. From 2015.
32.
by mrdrozdov 9y ago
IIRC a simple convnet will get higher than 99.0% on MNIST, so maybe it is more difficult than it seems at first glance :)
33.
by mrdrozdov 9y ago
An excellent read! I had the same thought. It's almost negligence not to mention it.
34.
by mrdrozdov 9y ago
Thanks for the clarification! I interpreted Rails as 'Rails' not 'Rail's' and 'template' as a verb (sorry for the specifics, but I'm at a linguistics conference after all). I've been using Pyrami
35.
by mrdrozdov 9y ago
Can you explain the arguments against Pyramid/Pylons?
36.
by mrdrozdov 9y ago
Do you think the community has professional writers or only writing enthusiasts from the tech world? :)
37.
by mrdrozdov 9y ago
At quick glance, this work seems related to Information Maximization like is done in the papers for InfoGAN, VIME, and Intrinsic Motivation (for automatic goal-setting in RL).
38.
by mrdrozdov 9y ago
Received: 20 August 2015 Accepted: 05 February 2016 Published online: 08 March 2016 Views: 19,562 Citations: 13
39.
by mrdrozdov 9y ago
I think LSTMs are far from being replaced. They work too well and are too simple to use.
40.
by mrdrozdov 9y ago
I'd put a little more faith in LSTMs. There's a lot less evidence for what they can't do than what they can. With enough fiddling, can get LSTMs to work for most tasks. I'm not sure they're quite as complicated as y
41.
by mrdrozdov 9y ago
This sounds like an argument that you can make for many fields. In data science, certainly you can cover topics like clustering and PCA, which are reasonably advanced, but straightforward to teach because they can be visualized very clearly
42.
by mrdrozdov 9y ago
I think it's worth considering data science as a discipline to teach youngsters rather than computer science. It's quite visual at times, requiring less abstract thinking, although does depend on important algorithmic principles t
43.
by mrdrozdov 9y ago
The research in CS is very active. Take a look at arXiv/cs to get a sense: https://arxiv.org/list/cs/recent I would say Machine Learning is particularly "hot" right now, with the anecdotal evidence
44.
by mrdrozdov 9y ago
I love this provocative title. For clarification, the author's argument is related to a person's ability to follow a path determined by intuition in the absence of proof. The examples given are of scientists who found ultimate suc
45.
by mrdrozdov 9y ago
I received a handful of emails specifically asking for the list of schools I was referring to. Here is a list of 31. There are certainly schools that I have missed, but I think that any of these programs offer a strong curriculum and commun
46.
by mrdrozdov 9y ago
I think there are good points here! > Spinning up some K80s on Azure, installing TF/CUDA/OpenCL I think a single k80 instance is roughly ~$1/hr. If you had an experiment running 24hrs a day for a year, you'd spend a l
47.
by mrdrozdov 9y ago
> And congratulations for you achievement and your bravery to quit the job! Thanks!
48.
by mrdrozdov 9y ago
I think this sentiment is roughly correct. It's basically the same for programmer as there are many jobs that involve programming. Note that data science doesn't necessarily mean machine learning, and there are lots of data scient
49.
by mrdrozdov 9y ago
I believe it is similar compensation as a software engineer with a specialty (front end, data engineering, databases, etc.). I would not recommend this career path to anyone that is in it for the money, but rather to do it because they find
50.
by mrdrozdov 9y ago
I did my undergrad at U. of Michigan, and my master's at NYU. I would highly recommend both programs, although there are many great ones out there. I probably have a list of ~30 schools that I think would be excellent for a master&#x27
51.
by mrdrozdov 9y ago
My undergrad was in computer science, although I would say this wasn't the case for most of my classmates. There were a lot of students from other engineering fields (like Mech E or civil), Math, Stats, physics, bio, and business.
52.
by mrdrozdov 9y ago
Stanford parser is very good for preprocessing data. Things like part of speech tagging, named entity recognition, and dependency parsing. If you want to do something fun and interesting with your data, you will probably need to implement i
53.
by mrdrozdov 9y ago
In Feb 2015 I quit my job as a front end developer to learn more about machine learning. First, I went through the Recurse Center, which is a 3 month program sort of like a writing retreat for programmers. I learned a lot about Python and A
54.
by mrdrozdov 9y ago
In this work Convolution Neural Nets (spatial models that have a weakly ordered context, as opposed to Recurrent Neural Nets which are sequential models that have a strongly ordered context) are demonstrated here to achieve State of the Art
55.
by mrdrozdov 9y ago
Is it so hard to publish at arxiv, share the paper on twitter, and submit a link to HN? At a minimum, arxiv is better than a blog post because there's some sense of versioning. For anyone who is curious, some conferences share the peer
56.
by mrdrozdov 10y ago
This is a really clever data collection technique. ;)
57.
by mrdrozdov 10y ago
From TNW. Seems relevant: Update 2, April 11, 11:15ET: Some readers have suggested that the allegedly deleted tweets might have initially appeared as replies to now-deleted tweets, which would explain why they are missing. However, numerous
58.
by mrdrozdov 10y ago
I am curious about DeepMind/Brain's role in the curating search results. Would you mind explaining more? Also, I am not sure I understand your reference to Youtube. Is this specifically about Youtube recommended videos in the side
59.
by mrdrozdov 10y ago
I follow this account: @gerfingerpoken Has not been censored at all on my newsfeed! That is the same account covered by this article: https://www.washingtonpost.com/business/economy/as-a-conserv... Tweets on avera
60.
by mrdrozdov 10y ago
I'm pretty skeptical this is true. I don't think this has to do with Twitter censoring left or right wing accounts.

More ›