dhammack

7 ms·

1.
by dhammack 2y ago
Was this summary generated by a LLM?
2.
by dhammack 3y ago
On the broader point though, there certainly are some companies that have access to unique datasets that they can take advantage of. Meta and Google come to mind as obvious ones, probably Microsoft too. Apple I'm less sure given their
3.
by dhammack 4y ago
Had a similar experience in middle school. We started initially doing net-send to specific users and having played around a little bit with batch files/command prompt before I tried net send *. Well the entire county was on the same ne
4.
by dhammack 5y ago
You can get the real content without the commentary at https://www.volcanodiscovery.com/lapalma/sep2021seismic-cris... Also see https://en.wikipedia.org/wiki/Cumbre_Vieja_tsunami_hazard for a good
5.
by dhammack 6y ago
Thanks for posting. In the early 2000's me, my brothers, and my dad spent several months taking pictures and submitting each sequential minute between 8-9. Looking back probably 20 years later now it is quite amusing to still see the p
6.
by dhammack 7y ago
Looks like only scoring code, not training :(
7.
by dhammack 8y ago
Voloridge Investment Management | Quant Analyst | Jupiter, FL | Full-time | ONSITE Voloridge Investment Management is an SEC registered investment advisor that implements bleeding edge machine learning techniques to solve the extremely chal
8.
by dhammack 8y ago
Hey I'm not familiar with this way of comparing classical and quantum computation. Can you point me to some more details? I have Nielson's book but don't remember seeing this analogy before!
9.
by dhammack 10y ago
I see. I still think it's easier to apply deepmind's feature transform on text rather than to try to invert a neural network. Armed with a network trained on Trump, deepmind's feature transform from text->network inputs, y
10.
by dhammack 10y ago
It seems like you're using WaveNet to do speech-to-text when we have better tools for that. To transfer text from Trump to Clinton, first run speech-to-text on Trump speech and then give that to a WaveNet trained on Clinton to generate
11.
by dhammack 11y ago
My hypothesis is that all RNNs (and in general complex dynamical systems) need to be reset periodically. If run for too long without resetting, they tend to get stuck in strange states, blow up, or cease activating. You can see this effect
12.
by dhammack 11y ago
It just finished, I got 4th. I used convnets. With the correct initialization and data preprocessing they actually are quite easy to train.
13.
by dhammack 11y ago
The other thing hedge funds offer is alternative markets to track. So while some may track the U.S. stock market (which you can get by just buying SPY), some funds track other markets like commodities, foreign equities, housing, bonds, etc.
14.
by dhammack 12y ago
If you think the true sentiment is symmetric, you can just change the decision threshold so that your algorithm answers positively about half the time. Just say positive when the sentiment is greater than the mean sentiment over your traini
15.
by dhammack 12y ago
You also may want to try betting on the spread between male-only companies and ones with at least one woman on the board. If the investment thesis is sound (board gender diversity is beneficial), this would be a good way to test it. It woul
16.
by dhammack 12y ago
#4 is the only one which I would say does not hold. I'd be very surprised if a firm of manual traders could consistently outperform the market (or beat a Sharpe of 1).
17.
by dhammack 12y ago
FYI - Hinton is doing an AMA on r/machinelearning on Monday!
18.
Nanex – 15-Oct-2014 – Treasury Flash Crash(nanex.net)
3 points by dhammack 12y ago|0 comments
19.
by dhammack 12y ago
Very impressive! Some cool ones it recognized: milk jug http://cdn-jpg.allyou.com/sites/default/files/image/2014/01/... ["milk", "gallon", "jug", "product&quo
20.
by dhammack 12y ago
I think it's rescaling all images to fit the training size. If that is the case, then when your image has very different dimensions it gets distorted and confused. Try something with a height/width ratio like the samples.
21.
by dhammack 13y ago
Your argument is more philosophical than mathematical. Lines are traditionally defined as the set of all points which satisfy some critera. In this case, a line is precisely made up of points.
22.
What are the 'real numbers', really?(math.vanderbilt.edu)
107 points by dhammack 13y ago|96 comments
23.
by dhammack 13y ago
Can you imagine the datasets FB has to work with? Statuses, pictures, locations, social graphs. It's incredible. It's hard to blame LeCun/Hinton/Ng for moving towards industry with data like that. I'd bet that the o
24.
by dhammack 13y ago
I like to think of the vectorized representation as just a nonlinear transformation to a higher dimensional space with a classifier afterwords. If you're familiar with linear algebra, then z = Wx, where W is a matrix of weights and x i
25.
by dhammack 13y ago
I've managed to get an intuition for backpropagation (the way gradients are computed for neural networks) using a similar analogy. The basic idea is that it's just a signal moving in the opposite direction in the network - it star
26.
by dhammack 13y ago
The test suite is amazing. https://github.com/ajalt/fuckitpy/blob/master/tests.py def test_chaining(): fuckit(fuckit('fuckit')) assert 'false' # Good t
27.
by dhammack 13y ago
It's up in North FL
28.
by dhammack 13y ago
Ah, that explains why the groupings are so good. That's cool, I hadn't thought about topic models outside of NLP before.
29.
by dhammack 13y ago
Nice! I assume LDA was run on the lyrics?
30.
by dhammack 13y ago
Did you roll your own version of t-sne to use in Erstaz? Now that I think about it, I've never seen a 3D visualization with t-sne before (and searching just now didn't find any), it could be that t-sne doesn't work as well in

More ›