stuartaxelowen

7 ms·

31.
by stuartaxelowen 3y ago
And not just data, but hard to match data generators. I’m skeptical of the defensibility of any given licensed dataset.
32.
by stuartaxelowen 3y ago
My preferred way of looking at this is “your project costs you this much to keep being alive”. An upfront cost means maintenance costs are essentially zero, resulting in your projects needing to hit a much lower bar to stay alive.
33.
by stuartaxelowen 3y ago
If you autoscale based on CPU consumption, doesn’t the macro level scaling achieve the same thing? Is the worry scaling small scale services where marginal scaling is a higher multiple, e.g. waste from unused capacity?
34.
by stuartaxelowen 3y ago
Many other replies here are wrong - the primary reason is that the LLMs were used on completely out of distribution data (e.g. trained on English, evaluated on completely different language that shared some characters). The points about com
35.
by stuartaxelowen 4y ago
Feature stores are essentially materialized views (aside from any realtime feature resolution needed). I think it's a good thing that there is specialized effort being taken here, though: features stores are an abstraction that could b
36.
by stuartaxelowen 4y ago
93 points in 4 hours... Why did this go off of the front page?
37.
by stuartaxelowen 4y ago
https://www.google.com/drive/ 's title does call it a "File Sharing Platform".
38.
by stuartaxelowen 4y ago
In my experience, the hard part is not extracting data from websites, but observing and implementing the actual structure of the site - e.g. iTunes categories have apps, which have reviews, etc, and making your scraper intelligent enough to
39.
by stuartaxelowen 4y ago
You don’t have to do the strict movement to raise the dumbbell, and can do less work by employing other muscles. For instance, using your legs and back to give the weight momentum to make it easier to raise it to the target position.
40.
by stuartaxelowen 4y ago
Incredible (but maybe unsurprising) that nature is maximizing information density and designing efficient codes (a’la information theory) depending on their noise environment!
41.
by stuartaxelowen 5y ago
Check out Spacy, it provides PoS tagging (among other things): https://spacy.io
42.
by stuartaxelowen 6y ago
Do you allow for on-premise inference?
43.
1M Podcast Review Dataset – Updated Monthly(kaggle.com)
2 points by stuartaxelowen 6y ago|0 comments
44.
by stuartaxelowen 6y ago
Thanks!
45.
by stuartaxelowen 6y ago
Very cool! Is this yours, OP? I built a text classifier platform ( https://www.taggit.io/ ) and I'd love to explore building something on top of this text stream to notify people of important happenings around the protes
46.
by stuartaxelowen 6y ago
I've always been fascinated at looking at public feedback, for products and companies. I've been working on a dashboard that lets you explore recent iOS app reviews: https://www.thoughtvector.io/vertext/ Ther
47.
by stuartaxelowen 6y ago
This feels like a perfect example of how contextual natural language is!
48.
by stuartaxelowen 7y ago
Is care of a sick family member "produced" in the same sense?
49.
Subword Tokenization – Handling Misspellings and Multilingual Data in NLP(thoughtvector.io)
3 points by stuartaxelowen 7y ago|0 comments
50.
Show HN: Phrase – a super fast phrase extraction app written in Rust(github.com)
2 points by stuartaxelowen 7y ago|0 comments
51.
by stuartaxelowen 7y ago
It's much better to just use a table of phrases/keywords, even if it takes a little more effort. Phrases are specific and have clear intent - using only the most common words leaves out critical context!
52.
by stuartaxelowen 8y ago
To fine tune the model to the specific customer needs. For example, learning the parameters of they user's face.
53.
by stuartaxelowen 8y ago
Check out Mari Kondo's show on Netflix! It is a guide on managing organizational debt in your home!
54.
by stuartaxelowen 8y ago
Why?
55.
by stuartaxelowen 8y ago
There are also topics like "NLP".
56.
by stuartaxelowen 8y ago
What changed was pretrained word embeddings like fasttext and GloVe. These embeddings drastically reduce the complexity of text classification problems, and reduce the amount of data you need to produce a high quality text classifier a gre
57.
by stuartaxelowen 8y ago
RE 5: I'd argue that with a relational database (barring extreme scale cases) is the perfect place for your business logic. Constraints, unique indexes, etc, all ensure that your data is always consistent with itself and with the busin
58.
by stuartaxelowen 8y ago
I think the author has it wrong, what the author sees as a "barrage of angry or ecstatic reviews" is actually a rich array of personal experiences waiting to be interpreted. Traditionally, this interpretation has been done wholly
59.
by stuartaxelowen 8y ago
Not so - the business is looking for real ROI, and thus will not be satisfied with topics that are "in the noise". Also, you create the measurement that establishes a problem before you apply the treatment (create new feature X o
60.
by stuartaxelowen 8y ago
Data science is about drawing conclusions from data, not the size of it. Most data science happens on datasets numbered in the hundreds rather than those in the millions.

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