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Hot data science related posts every hour. Chat: https://telegram.me/r_channels
[OC] The Most Valuable Companies In The World
/r/Infographics
https://redd.it/zly6d0
Just a population density map with different colors :)
/r/dataisugly
https://redd.it/zkk8n2
Top-earning YouTube channel in every country
/r/MapPorn
https://redd.it/zlripw
Sun Tanning vs. Skin Whitening google search
/r/MapPorn
https://redd.it/zm2fl1
What's your opinion of medical marijuana, as a former patient? (Former medical marijuana patients)
(M/F)
Hello! I am a research student researching opinions on medical marijuana from former patients. I would really appreciate it if you would take my survey. Thank you! https://forms.gle/V7mAacDWBAcBcNCW8
/r/SampleSize
https://redd.it/zlwj13
Dataset: 2,889 battles occurring within Japan during its Warring-States period, from 1467 to 1600.
https://www.tandfonline.com/doi/abs/10.1080/03050629.2023.2149514?journalCode=gini20
/r/datasets
https://redd.it/zlorxu
If you were to hire a Data Scientist with one question, what would that be?
let's get creative 😀
/r/datascience
https://redd.it/zll3sq
America’s Beautiful Weather Zones by Mattie Lubchansky
/r/MapPorn
https://redd.it/zlqmpb
P Implemented Vision Transformers 🚀 from scratch using TensorFlow 2.x
Hello Everyone 👋,
I just implemented the paper named AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE popularly known as the vision transformer paper. This paper uses a Transformer encoder for image recognition. It achieves state-of-the-art performance without using convolutional layers given that we have a huge dataset and enough computational resources.
Below I am sharing my implementation of this paper, please have a look and give it a 🌟 if you like it. This implementation provides easy-to-read code for understanding how the model works internally.
My implementation: GitHub Link
Thanks for your attention. 😀
/r/MachineLearning
https://redd.it/zloof9
[OC] It takes over 12,000L of water to produce one outfit (not including shoes or underwear) - the average person drinks 691L of water a year. That's over 18 years worth of drinking water
/r/dataisbeautiful
https://redd.it/zln5zj
[OC] Meat consumption
/r/dataisbeautiful
https://redd.it/zlgxjk
[OC] Prevalence of British and American Spelling Variants on Wikipedia
/r/dataisbeautiful
https://redd.it/zlc972
Tesla value as it relates to Twitter's purchase [OC]
/r/dataisbeautiful
https://redd.it/zl0t0n
'Time lapse' diagram of the motion of arms & string of an 'inswinger' ballista.
/r/mathpics
https://redd.it/zioaru
the Richest Billionaires in Each Country
/r/visualization
https://redd.it/ziopas
Work find it unbelievable I’ve exceeded 75GB storage limit…?
IT almost laughed at me on the phone saying I’d need at least 200GB.
I asked the bloke what his PC at home goes up to, and he implied most storage is taken up by programs so 75GB is more than enough for files.
Nobody in this organisation (4000+ people) have ever exceeded 75GB. Wtf??
One typical csv file is 1GB, how is this happening in such a large organisation?? My god.
Edit: this is Onedrive space. We’re unable to store things locally
/r/datascience
https://redd.it/zm3gac
[OC] Over the last decade, Chile has risen to become the world's third-largest producer of cherries, only behind Turkey and the United States. 🍒
/r/dataisbeautiful
https://redd.it/zlzr8q
Religious affiliation in Iran, based on a 2020 survey by Gamaan Research.
/r/Infographics
https://redd.it/zlse2t
Found the image on Twitter. Posted by climate change deniers
/r/dataisugly
https://redd.it/zkqci5
[OC] The Most Valuable Companies In The World
/r/dataisbeautiful
https://redd.it/zly6c2
Project Run and fine-tune BLOOM-176B at home using a peer-to-peer network
We made a library for inference/fine-tuning of open 175B+ language models (like BLOOM) using Colab or a desktop GPU. You join forces with other people over the Internet (BitTorrent-style), each running a small part of model layers. Check out our Colab example!
Thing is, even though BLOOM weights were publicly released, it was extremely difficult to run inference efficiently unless you had lots of hardware to load the entire model into the GPU memory (you need at least 3x A100 or 8x 3090 GPUs). E.g., in case of offloading, you can only reach the speed of \~10 sec/step for sequential (non-parallel) generation.
A possible alternative is to use APIs, but they are paid and not always flexible (you can’t adopt new fine-tuning/sampling methods or take a look at hidden states). So, Petals come to the rescue!
This is how Petals work: some peers want to use a pretrained LM to solve various tasks with texts in natural or programming languages. They do it with help of other peers, who hold subsets of model layers on their GPUs.
More details:
Paper (with speed measurements): [https://arxiv.org/abs/2209.01188](https://arxiv.org/abs/2209.01188)
GitHub repo: https://github.com/bigscience-workshop/petals
What do you think of it?
/r/MachineLearning
https://redd.it/zl03b0
What does yellow signify?!
/r/dataisugly
https://redd.it/zlarj4
Relative Humidity readings from my basement after carrying out remedial works - love that trend [OC]
/r/dataisbeautiful
https://redd.it/zlniy0
Lying on the CV taken to the next level
I have someone in my team who is currently applying for one of the internal roles - a promotion 2 levels above her current level. I am on the interview panel but not her referee and therefore have to remain unbiased and take the information that was presented in the CV like I would for an external applicant.
This person has no technical skills, no understanding behind even simple concepts, just memorized a few things but is very interested in promotions and started asking about them 6 months into the role. Seems way more interested in promotions than learning DS :(
Anyway, I have seen plenty of people add about 20% to their CV, overstate their role in a project etc. This person has claimed that she has built 2 models that don't exist as a part of my team. She described techniques used and claims she has led the whole effort and the models are now deployed (these are techniques that I mentioned in team meetings, but always said that it will depend on the data. Turns out we didn't have enough good data so looks like these models will never be built. She is up to date on these developments). I am in a very large org and nobody really keeps track of new models etc.
On the basis of these lies, I have seen that she was invited for an interview. Many people that are way more talented but were more honest didn't. This really bothers me. I did mention it to my manager who seems disinterested and made a comment that I need to be building up junior DS and not tearing them down :(
This is more of a vent than anything.
/r/datascience
https://redd.it/zlobg8
The United States as James K. Polk Wanted It [964 x 740]
http://i.imgur.com/pwXoy.jpg
/r/MapPorn
https://redd.it/zkxfos
[OC] geospatial distribution of different fast food chains in the USA (included some of your suggestions from my previous post)
/r/dataisbeautiful
https://redd.it/zl3bta
Statisticians who got their PhD and now work in industry, how is it like? Q
Curious as to how the transition to industry was after a phd in statistics. Exciting? Frustrating? I’ve often heard both sides as with your phd you get more lucrative data science roles, but also it can be frustrating as there’s no emphasis of statistical rigor in industry. What have been your experiences? Any of you in startups? Developed your own startup? I’m just curious to see what kind of non traditional placements occurred for people who got their PhD in statistics.
/r/statistics
https://redd.it/zkzol0
[OC] Median home price in each U.S. State 2022.
/r/dataisbeautiful
https://redd.it/zl194o
When did women get voting rights? Portugal had a dictatorship in the 70s, but what happened in Switzerland for it to happen so late??
/r/MapPorn
https://redd.it/zkugqa
[OC] The most frequently mentioned books in posts on r/books
https://redd.it/zkwcsc
@datascientology
