etrain

8 ms·

31.
The alternating descent conditional gradient method(stat.berkeley.edu)
3 points by etrain 11y ago|0 comments
32.
Readings in Database Systems, 5th edition(redbook.io)
1 points by etrain 11y ago|0 comments
33.
by etrain 11y ago
Short answer is actually: maybe! The bulk of the work done in this code (in terms of FLOPS and, likely, wall-clock time) is going to be in BLAS-3 operations in the feed-forward and back-prop steps. That is, almost all of the work is done us
34.
by etrain 11y ago
I published a paper this year that used it directly and even proposed a modification of it to model job time for a distributed execution environment with a centralized master governing scheduling, task serialization, etc. When you talk scal
35.
by etrain 11y ago
Fiduciary Duty.
36.
by etrain 11y ago
Spanner maxes out at ~10 transaction (batches)/second in that paper. By comparison, a standard PostgresSQL installation (on littleish iron) is capable of 50k TPS depending on the benchmark you're running. In the Spanner design the
37.
by etrain 11y ago
I'm a researcher in the same basic field, and I too am pretty good at this "bullshittery." However, Philip trivializes the "10,000 hours" spent learning this stuff as merely learning how to cope with this interface.
38.
by etrain 11y ago
The Mozilla Suite existed and wasn't that bad, even in 99/00 when I started using Linux. Konqueror was also pretty decent. The web also wasn't as central to the Internet experience then (irc, pop3 clients, and so on were pret
39.
by etrain 11y ago
Ion Stoica's Big Data Systems research class at Berkeley has a pretty solid reading list to get you started: http://www.cs.berkeley.edu/~istoica/classes/cs294/15/class.h...
40.
by etrain 11y ago
EBITDA.
41.
by etrain 11y ago
Check out our project, [KeystoneML]( http://keystone-ml.org/ ) - it's geared to large scale machine learning in the realms of computer vision, NLP, and (soon) speech. The design is modular and engineering-friendly and is
42.
by etrain 11y ago
This is the heart and soul of quantitative investment management.
43.
by etrain 11y ago
I've heard this referred to as "the footstool" and "the emasculator."
44.
by etrain 11y ago
For anyone else confused about the Twitter numbers reported in Table 1, I believe it should read "0.5-15 billion tweets/day" as opposed to "0.5-15 billion tweets/year" on the first line, which is consistent wit
45.
by etrain 11y ago
There are a handful of Chinese index ETFs out there - FXI is the biggest one - but there are others you can probably short like the MCHI or the GXC. If you really like to gamble you can buy puts on some of the bigger ones.
46.
by etrain 11y ago
Yeah, I agree this is surprising. If all the data in their CPU implementation were stored in a well-packed columnar format, I'd expect something close to theoretical memory throughput on the query they describe (intermediate counts tab
47.
by etrain 11y ago
Nope! KeystoneML is a research project exploring how to best support end-to-end pipelines for large scale machine learning. It's complementary to MLlib and some functionality from KeystoneML may find its way into MLlib in the future.
48.
by etrain 11y ago
I'm one of the authors of KeystoneML. Happy to answer any questions about it here.
49.
by etrain 11y ago
The chosen benchmark (a customer reviews) table is likely something that benefits tremendously from compressed columnar storage: 1) It has a small number of attributes which are almost always present in the records - that is, a fixed schema
50.
by etrain 12y ago
This does not mean they are not using Vowpal Wabbit. It is very easy to run Vowpal Wabbit with a logistic loss function. Also, vw is what I'd consider "industry standard."
51.
by etrain 12y ago
My guess is they're using liblinear or vowpal wabbit under the hood. Both support SGD-based learning and work well in a streaming setting where data could be on disk or in memory.
52.
by etrain 12y ago
Agreed, but given their differences, the historical correlation between the S&P and the dow is absurd. https://www.google.com/finance?q=INDEXDJX%3A.DJI&ei=Wef5VPmw...
53.
by etrain 12y ago
oh, with my latest chess programming language, the source code for chess is 0 bytes. feed an empty file to the chess compiler and out comes a working binary which plays chess.
54.
by etrain 12y ago
The authors are selling a product to detect click bots. Take the results with a grain of salt..
55.
by etrain 12y ago
Based on the video, it looks like these guys likely swap out for the newest equipment as soon as it makes sense economically. If I were making such an investment, I'd think carefully about the rate of computational depreciation (which
56.
by etrain 12y ago
Cool read - but you should be careful modeling Tables as things with an "id". The relational algebra is about sets of tuples. The fact that there is an index that one can use to make joins go faster is an (important) implementat
57.
by etrain 12y ago
See Byzantine Fault Tolerance if you don't want to deal with a centralized ledger: http://en.wikipedia.org/wiki/Byzantine_fault_tolerance
58.
by etrain 12y ago
Various linear solvers (either via normal equations, QR, etc.) all have really fast multi-threaded implementations in, e.g. OpenBLAS. These could directly benefit lm() and glm(). That said - there's no reason why you couldn't alr
59.
by etrain 12y ago
Check out SparkR - http://amplab-extras.github.io/SparkR-pkg/
60.
by etrain 12y ago
One thing often missing from these types of solutions (this one included) is that it's extremely easy to generalize them beyond the 9x9 case.

More ›