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Hot data science related posts every hour. Chat: https://telegram.me/r_channels Contacts: @lgyanf
Putting the population of Manhattan and the Dakotas side by side
/r/MapPorn
https://redd.it/z4iwmq
Rivers of South America [OC]
/r/dataisbeautiful
https://redd.it/z4l1pj
D First time NeurIPS
I am going to NeurIPS next week. This is the first time I am going to an AI conference, and the first time I am going to a very large conference. I did my PhD in pure math, so I have been to plenty of academic conferences, but they were all smaller (less than 100 people) events. I am presenting a workshop paper and am going alone from Europe.
Anyone have any general tips when going to a large AI conference for the first time?
It would be nice to find some people to have lunch with, or eat dinner with, because in my experience you learn at least as much by talking to people as you do from academic presentations. So I am curious on how the social interactions at these conferences are: do people hang out mostly with their own crowds, or is it easy to get in touch with new people?
I am also vaguely looking for interesting people and places where I might go on a research stay (paid by my job) some time in the future, so that is another motivation for meeting people.
/r/MachineLearning
https://redd.it/z48t6e
According to Twitter, Twitter’s algorithm favours conservatives - Its data shows a bias aiding unreliable media, regardless of ideology, and right-wing political parties
https://www.economist.com/graphic-detail/2021/11/13/according-to-twitter-twitters-algorithm-favours-conservatives
/r/dataisbeautiful
https://redd.it/z4bi13
What Python Libraries I can use to produce such markers on a map. The marker should also have a popup functionality.
/r/visualization
https://redd.it/z44ql0
In 1996 the Australia Government implemented stricter gun control and restrictions. The numbers don't lie and proves it worked.
https://redd.it/z489n8
@datascientology
Looking for a dataset that is updated once a day ( not : stocks,crypto,weather)
Please provide a link if possible. Thanks
/r/datasets
https://redd.it/z2fjby
Weekly Entering & Transitioning - Thread 21 Nov, 2022 - 28 Nov, 2022
Welcome to this week's entering & transitioning thread! This thread is for any questions about getting started, studying, or transitioning into the data science field. Topics include:
* Learning resources (e.g. books, tutorials, videos)
* Traditional education (e.g. schools, degrees, electives)
* Alternative education (e.g. online courses, bootcamps)
* Job search questions (e.g. resumes, applying, career prospects)
* Elementary questions (e.g. where to start, what next)
While you wait for answers from the community, check out the [FAQ](https://www.reddit.com/r/datascience/wiki/frequently-asked-questions) and Resources pages on our wiki. You can also search for answers in [past weekly threads](https://www.reddit.com/r/datascience/search?q=weekly%20thread&restrict_sr=1&sort=new).
/r/datascience
https://redd.it/z0q0yl
20 Remarkable World Productivity Statistics
/r/Infographics
https://redd.it/z407e6
What is easier to master: the math or the technologies required in the field?
/r/datascience
https://redd.it/z3wndg
[OC] BlackRock is the world's largest asset manager - breaking down how it makes money
/r/dataisbeautiful
https://redd.it/z3wycr
For data explorers, here's a cool dataset!
Exercise your data munging skills by playing with this cool dataset I discovered.
https://www.kaggle.com/datasets/amorybristol/playboy-playmate-features
/r/datasets
https://redd.it/z3mds7
European words for “water”
/r/MapPorn
https://redd.it/z3pg9i
Birth of a Solar System
/r/Infographics
https://redd.it/z3kt7u
Happy Cakeday, r/dataisugly! Today you're 10
Let's look back at some memorable moments and interesting insights from last year.
Your top 10 posts:
"[they've taken over](https://www.reddit.com/r/dataisugly/comments/wn5wbm)" by [u/FourFans0fFreedom](https://www.reddit.com/user/FourFans0fFreedom)
"When the White House just slips an extra 0.5 in there for good measure (y-axis madness)" by u/chipmonkey75
"[Ontario newspaper survey. That is a hefty 1%](https://www.reddit.com/r/dataisugly/comments/rvxv38)" by [u/A\_Bridgeburner](https://www.reddit.com/user/A_Bridgeburner)
"Who visualises their data like that?"
"[Spotify Wrapped](https://www.reddit.com/r/dataisugly/comments/r6n53a)" by [u/constrito](https://www.reddit.com/user/constrito)
"Car paint color popularity by percentage of sales over time. Did they even think?" by u/Injustpotato
"[Absolutely 0 understanding of pie charts](https://www.reddit.com/r/dataisugly/comments/v6vuw8)" by [u/Wolffie231](https://www.reddit.com/user/Wolffie231)
"Gradient legend colors! How come nobody thought of this before? Brilliant" by u/ICatchx22I
"[This tabloid monstrosity](https://www.reddit.com/r/dataisugly/comments/vlrhmn)" by [u/MangoJibango](https://www.reddit.com/user/MangoJibango)
"USA, are you ok?" by u/valriser
/r/dataisugly
https://redd.it/z2szmt
R Robust Learning: the past and present. The DNN has strong fitting capability, but we find ...
ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State
arXiv: https://arxiv.org/abs/2207.00118
Code: https://github.com/XinshaoAmosWang/DeepCriticalLearning
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https://preview.redd.it/0vtst8xkk22a1.png?width=1181&format=png&auto=webp&s=a07a443bb633e7efacd4c41d7941ec6591e46d25
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https://preview.redd.it/jtc5n5jlk22a1.png?width=1195&format=png&auto=webp&s=2643109cd59c2b130bbd3f8d8aaeb7054a68ec3e
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https://preview.redd.it/3c8c9i7mk22a1.png?width=1239&format=png&auto=webp&s=c670ab84fe973ae34326b8cd86652c25ebaadc50
/r/MachineLearning
https://redd.it/z49k7x
Q What is the logic behind a low p-value being favorable?
I need help understanding why a low P-value is favorable. Wouldn't you want a high P-value to indicate that the experiment results are consistent with the same experiment being run on other sample groups? For example, If I found there was a 4" height difference between men and women in my sample group, and I ran the t-test and got a p-value of 0.95, wouldn't that mean that 19/20 times this experiment is run on different sample groups there will be a 4" height difference? So I could say more confidently that there is in fact, a 4" height difference?
/r/statistics
https://redd.it/z3yohh
Hey I wanna learn Statistics with python can anyone suggest me a good book and a good YouTube tutorial because i am really poor at it I don't know the basic concepts about it
/r/datascience
https://redd.it/z48nvb
158 different motorcycle brands from different coutries
/r/Infographics
https://redd.it/z31ngw
Biggest religions in India by district, 2011
/r/MapPorn
https://redd.it/z44k0v
R Category Theory for AI,AI for Category theory
I have uploaded two repositories on github - the code was personal so it's pretty much undocumented but due to personal issues I currently can't work on them and maybe the ideas here will inspire someone.
The main ideas are:
1) Seeing categories as ensembles of ml models with more complex sturcture than X->(Y1,Y2,...) and using commutative diagrams as optimizations objectives with equality of morpisms (=models) replaces with some loss/objective function.
https://github.com/BeNikis/Category-Theoretic-Model-Ensembles
2) Using language models and some formal language for describing categories,automating the above work when we have some base category with some 2nd level (for example in a category with only tensors we could have two objects of different patches of an image of the same size,the shape of those patches can be '2nd level' types of the objects and we could apply any morphism that takes in the type of that object) we could automatically find pathways (compositions of models) that do what we want.or,if the category we;re working is for example is Hask,Haskell types and programs,this could be used in automated programming.
https://github.com/BeNikis/Manipulating-Categories-With-ML
3)I have this very general concept of a agent environment adjunction - an adjunction in category theory is a very loose but deep relationship between two categories,basically 'an isomorphism up to a specified morphism' . in the agent-environemnt case,the agen percieving the environmet is the forgetful functor (in reference to the mane free-forgetful adjunctions) because we unavoidably lose some information when we percieve with limited sensors,and inferring the overall state of the environment from the agents known information would be the free functor. Now,combining this with the above two ideas,the two categories could be the categories of categories of states of the environemnt and for the agent ml model ensembles,the adjunction itself could be seen as an optimization objective (the information from the sensors of the agent are injected into the category by the DataMorphism class in the first repo),and we could build better and better agent states by building up that categories with (co)limits,which again are fuzzified with some yet unknown unsupervised obejctive.
This idea is similar to what is already happening in both ML and CT - on the ML side we have autoencoders and diffusion models which go from environment->'agent' (some intemediary code)->back to environemnt,and in CT for example we have this paper on a syntax-semantics view of language models,which rings bells with similarities with the syntax-semantics adjunction in categorical logic:
https://arxiv.org/abs/2106.07890
I'm posting this due to personal stuff and because I'm currently on the edge of exhaustion working on this stuff,so maybe bringing these ideas up will not let them go to waste if they're valuable in the first place.
/r/MachineLearning
https://redd.it/z2hr4c
Infographic- The Benefits of Utilising Metal Raised Beds
/r/Infographics
https://redd.it/z480hx
[P] Free Stable Diffusion 2.0 hosted interface
When Stable Diffusion 2.0 was released last night, we knew we wanted to get it into production as quickly as possible so that the ML community could use a free web interface to experiment with the model. And don't worry, there is no sign-in, email, or credit card required to use the demo as much as you want.
Baseten's previous Stable Diffusion demos have been used to create more than a quarter million images, but the best of them are already being blown away by the quality of images Stable Diffusion 2 produces. Try it for yourself ... let's see what you've made in comments!
Give Stable Diffusion 2 a try here: https://app.baseten.co/apps/VBlnMVP/operator_views/nBrd8zP
/r/MachineLearning
https://redd.it/z3xpd2
Are European Nations Drinking Less in Recent Years? (interactive version in comment) [OC]
/r/dataisbeautiful
https://redd.it/z3lcmx
Q Why is the graph of the residuals and predicted values (y-hat) used to check the assumption of linearity for multiple linear regression?
I understand that when the residuals follow no pattern across the range of the predicted y values (basically it's random), then the predictors/regressors (deterministic portion) of the model are doing a good job predicting the outcome variable.
However, when there is a non-random pattern, why is this taken as evidence that the outcome variable does not have a linear relationship with the model's predictor variables (aka. not satisfying the linearity assumption)?
EDIT: Ok i realize I might have been unclear in my question. Let me try again.
The recommendation to check the linear assumption of a linear regression with one predictor - one response variable is to just plot the scatterplot of these two variables (x-y plot).
However, when there are two or more predictor variables, the recommendation is to examine the residuals - predicted response variable plot. My question is, how can a random pattern of the residuals in this plot tell us that the combination of all the predictor variables has a linear relationship with the response variable, (the linearity assumption)?
/r/statistics
https://redd.it/z307yn
Why Does the U.S. Have So Many Mass Shootings? Research Is Clear: Guns. (Published 2017)
https://www.nytimes.com/2017/11/07/world/americas/mass-shootings-us-international.html?unlocked_article_code=t7FfpC4U5Ax0fHG1Bm-_A3W_kP_zOvxOeSuGBWpgGxFNEp2_02EzBbZF3bTUWiFqvlx-z52RFYC0h08foVdcrj5Amg4zn4MgH4OZMGWQqnQA6pHbhHrwaeLVLiWi9BMzJB0jbxeHgHSUcBOQQWRJy7Hv1r-dfGlHNw28yBQSPR8qDYz40R__GjH0DcsmuhBC7kAlQvDf-bpd1zJHGdYUDgyrPty6F8ZUXEnpN_AbDorv0-gr-g0JMMg7rPSwzpIQn1svaHRPmfzIcQHT-BxonfAgKTaRt_J11eS2_1OP1oYtUbDmy8rQ9DDF4AoB2N20DwYWlFdfHHH75B4fB-ov7KcMhM5V6jCarOksbcCZXEY&smid=share-url
/r/dataisbeautiful
https://redd.it/z3ogaz
D Informal meetup at NeurIPS next week
Anyone headed to NeurIPS in New Orleans next week? If people are interested, it'll be good to arrange an informal meetup. Happy for suggestions on location and time.
/r/MachineLearning
https://redd.it/z3huy4
Can I become a data scientist without a masters?
I am a data analyst at a growing advertising company who wants to be a data scientist making 100k. I think I can get promoted to that job title while here and was wondering if that would help me land other data scientist roles later on, despite the fact that I don’t have a masters? I have a BS in stats from a top university, a great gpa, and have a lot of programming experience in my current job doing ML and automating data cleaning processes.
/r/datascience
https://redd.it/z3n3ej
C Why is statistical programmer salary in the USA higher than in Europe?
I think average for a middle level statistical programmer is 100K in the USA while middles in Europe would receive just 50-60K. And for seniors they will normally be paid 100-150K in USA, while in Europe 80-90K at most.
/r/statistics
https://redd.it/z3gm91
Anyone in this subreddit self taught?
I’m a self taught programmer (no college degree) who eventually broke into the ML/AI field as a ML engineer. I was wondering who else here is self taught, and if so, what was your journey?
/r/datascience
https://redd.it/z37m2h
