This is very much what dspy aims to address. Learning the incantations necessary to prompt well can be replaced by an algorithmic loop and example labelled cases.
This paper is always useful to remember when someone tells you to just use the cosine similarity: https://arxiv.org/abs/2403.05440 Sure, it’s useful but make sure it’s appropriate for your embeddings and remember to run…
Created by Steve Ruiz, his timeline is full of little interesting thoughts played out in the development of this, a fascinating insight into the minutiae that users take for granted but that make the difference:…
My guess is something very similar to U2Net https://github.com/xuebinqin/U-2-Net
systems also need noisy labels..
Transformers suffer from a quadratic bottleneck when calculating attention. Much work has been done investigating where memory can be saved by being more explicit on which attentions to calculate. This repo implements…
ha! no way - I just cited your paper in the comments without noticing you already had.
for all common models (GloVe, fastText, word2vec) the means across word embeddings are tightly concentrated around zero (relative to their dimensions), thus making the widely used cosine similarity practically…
The author of FastAPI https://twitter.com/tiangolo is a Spacy employee
as a reference: https://twitter.com/AndrewM_Webb/status/1183150368945049605
You can train programmers because there is time and there aren’t often lives at stake. Nurses have to hit the ground running in specialist areas where there is (I’d assume) often little time to acquire all necessary…
Is this not a timeseries classification, which they do implement?
At this point, the UK is very much in the same state as the US.
This is what the mypersonality dataset collected on Facebook was. The 5 axes of OCEAN - Openness, Conscientiousness, Extroversion, Agreeableness and Neuroticism for all those who took the test.
The T-Sne view for papers is a killer feature. Loved it for ICLR, glad more will be able to use it. Thank You!
Working on a blood sugar tracker for my wife as an excuse to practice with flask/sqlalchemy
Wanted to see what Facebook Scuba was, and found this comment thread that really highlights the point of this blog. https://news.ycombinator.com/item?id=13463016
I use FastAPI, its awesome - thank you, just saying...
i think the ReMarkable might be of interest
Thanks for opps daily, i was a subscriber and enjoyed thinking through the problems posted but as with others here was too busy to commit pen to paper on any of them. good luck with topstonks.com
blog post with a better explanation of what's going on from Stephen Few - the designer of the bullet chart: https://www.perceptualedge.com/articles/misc/Bullet_Graph_De...
https://youtu.be/cBjpROfzoos?t=14m20s because I both love the fast show and a challenge
That is how ASOS started, As Seen On Screen. Now doing over 2 billion in turnover a year
There may be merit in tagging each of the words with their part of speech prior to fitting the model in a similar way to sense2vec. Using your example above you would then have 2 vectors, one for leaves|VERB and one for…
from his portfolio: https://pro2-bar-s3-cdn-cf1.myportfolio.com/483775244caf67b7...
This is very much what dspy aims to address. Learning the incantations necessary to prompt well can be replaced by an algorithmic loop and example labelled cases.
This paper is always useful to remember when someone tells you to just use the cosine similarity: https://arxiv.org/abs/2403.05440 Sure, it’s useful but make sure it’s appropriate for your embeddings and remember to run…
Created by Steve Ruiz, his timeline is full of little interesting thoughts played out in the development of this, a fascinating insight into the minutiae that users take for granted but that make the difference:…
My guess is something very similar to U2Net https://github.com/xuebinqin/U-2-Net
systems also need noisy labels..
Transformers suffer from a quadratic bottleneck when calculating attention. Much work has been done investigating where memory can be saved by being more explicit on which attentions to calculate. This repo implements…
ha! no way - I just cited your paper in the comments without noticing you already had.
for all common models (GloVe, fastText, word2vec) the means across word embeddings are tightly concentrated around zero (relative to their dimensions), thus making the widely used cosine similarity practically…
The author of FastAPI https://twitter.com/tiangolo is a Spacy employee
as a reference: https://twitter.com/AndrewM_Webb/status/1183150368945049605
You can train programmers because there is time and there aren’t often lives at stake. Nurses have to hit the ground running in specialist areas where there is (I’d assume) often little time to acquire all necessary…
Is this not a timeseries classification, which they do implement?
At this point, the UK is very much in the same state as the US.
This is what the mypersonality dataset collected on Facebook was. The 5 axes of OCEAN - Openness, Conscientiousness, Extroversion, Agreeableness and Neuroticism for all those who took the test.
The T-Sne view for papers is a killer feature. Loved it for ICLR, glad more will be able to use it. Thank You!
Working on a blood sugar tracker for my wife as an excuse to practice with flask/sqlalchemy
Wanted to see what Facebook Scuba was, and found this comment thread that really highlights the point of this blog. https://news.ycombinator.com/item?id=13463016
I use FastAPI, its awesome - thank you, just saying...
i think the ReMarkable might be of interest
Thanks for opps daily, i was a subscriber and enjoyed thinking through the problems posted but as with others here was too busy to commit pen to paper on any of them. good luck with topstonks.com
blog post with a better explanation of what's going on from Stephen Few - the designer of the bullet chart: https://www.perceptualedge.com/articles/misc/Bullet_Graph_De...
https://youtu.be/cBjpROfzoos?t=14m20s because I both love the fast show and a challenge
That is how ASOS started, As Seen On Screen. Now doing over 2 billion in turnover a year
There may be merit in tagging each of the words with their part of speech prior to fitting the model in a similar way to sense2vec. Using your example above you would then have 2 vectors, one for leaves|VERB and one for…
from his portfolio: https://pro2-bar-s3-cdn-cf1.myportfolio.com/483775244caf67b7...