rpedela

39 ms·

361.
by rpedela 9y ago
Annual AWS conference is happening right now which is when they announce/release a lot of new services.
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by rpedela 9y ago
The point is when you don't know what you are looking for exactly. The Abobe products are a good example of this. I don't use Photoshop often so I forget where stuff is. Having a lot of menus and each menu having a large number of
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by rpedela 9y ago
Unlimited access meaning I can access the website as often as I want without having to pay more money.
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by rpedela 9y ago
I don't know if internet access is a "right", but it is pretty important in a modern society because internet access equals information access. It is hard to be an informed, productive participant in a modern society without
365.
by rpedela 9y ago
Setting random_page_cost = 1 is pretty common advice for SSD and works well in my experience. Typical advice for HDD RAID or SCSI is random_page_cost = 2 and SSDs are faster than them.
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by rpedela 9y ago
Isn't there significant amounts of ice at the poles?
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by rpedela 9y ago
Can this sync to any ES server or just appbase? All the examples in docs are for appbase targets.
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by rpedela 9y ago
I think it is good to remain skeptical about medical ML, but I don't think it should be because of the algorithms or that retrospective analysis is flawed. In most retrospective medical research, the data sizes are relatively small. Li
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by rpedela 9y ago
Technically yes. It will find words which are used in similar contexts such as synonyms, antonyms, etc. However in practice, word2vec and clustering does a good job of finding synonyms [1]. 1. https://www.slideshare.net/mobi
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by rpedela 9y ago
Essentially they are useful for comparing the semantic similarity of pieces of text. The text could be a word, phrase, sentence, paragraph, or document. One practical use case is semantic keyword search where the vectors can be used to auto
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by rpedela 9y ago
gensim is one of the best libraries for word vectors and summarization. For parsing and NER, Stanford CoreNLP works best in my experience.
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by rpedela 9y ago
Using Chrome on both a Chromebook and Galaxy S5, the right sidebar is screwed up. On the phone, it completely blocks the content.
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by rpedela 9y ago
Never used it but seems promising: https://github.com/NLPchina/elasticsearch-sql
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by rpedela 9y ago
True, but some of those space predictions may come true sooner rather later now largely thanks to Elon and SpaceX. For example, a startup called Positron Dynamics has a serious proposal for an antimatter rocket. If it works, that would like
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by rpedela 9y ago
I agree. An easy workaround is to add "mdn" to the search.
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by rpedela 9y ago
The Office ribbon was/is far superior to the original, flat UI for novice and intermediate users. By flat, I don't mean aesthetics but rather the layout. Every menu option and button had the same visual importance of every other b
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by rpedela 9y ago
If you already have a database, I recommend storing the session in the database. I personally use connect-pg-simple when using Postgres. In other words, I wouldn't add another major dependency like Redis just for session storage.
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by rpedela 9y ago
I have written a lot of React and JSX. I have never used that pattern and don't plan to start. I use JS for my conditionals and never put them in JSX. Unless it is a extremely trivial component, I find putting logic inside JSX hard to
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by rpedela 9y ago
Best one I have found. https://github.com/coolwanglu/pdf2htmlEX
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by rpedela 9y ago
There is also sense2vec which I think tries to do something similar. https://explosion.ai/blog/sense2vec-with-spacy
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by rpedela 9y ago
No idea. Experiment!
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by rpedela 9y ago
Each token would have one vector from word2vec. A token could be a word or phrase depending on the pre-processing. The words in a phrase are usually concatenated with an underscore. I recommend gensim if want/need phrases.
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by rpedela 9y ago
Why not Airborn Docs? I wouldn't worry about the misspelling either. Google is a misspelling, lots of music groups are misspelled words, etc.
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by rpedela 9y ago
Or stop making assumptions with very little information. That is really my point, but I didn't think it needed to be said explicitly.
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by rpedela 9y ago
Can't speak for xstartup, but if the dataset is 100+ GB then EBS, S3, etc start to cost real money. I host an application on AWS for a single customer with 10 active users which costs about $1500/month because there are several T
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by rpedela 9y ago
Yeah I agree that they appear to be missing a designer or product developer. More concretely, they need someone who is imaginative and can see the big picture from a user/customer perspective.
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by rpedela 9y ago
I think using word vectors for lemmatization is an interesting idea and on the cutting edge. Here is a paper which discusses it. https://link.springer.com/chapter/10.1007/978-3-662-49192-8_...
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by rpedela 9y ago
Lucene is faster and better than grep too. Annoy may be better than Lucene's "more like this" query which is for finding similar documents in an index to a given set of documents. But how would it be helpful for keyword searc
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by rpedela 9y ago
For English NLP, I often stem first because it usually reduces noise. I think your main concern is that the abundance of homonyms will increase noise which is certainly possible. Because I don't know Hebrew, I don't have any intui
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by rpedela 9y ago
I don't know anything about Hebrew. But maybe lemmatization would be better than stemming since it takes meaning and context into account. It is also possible that it is an unnecessary step for clustered word vectors for your use case.

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