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Hot data science related posts every hour. Chat: https://telegram.me/r_channels
[N] Compromised PyTorch-nightly dependency
https://pytorch.org/blog/compromised-nightly-dependency/
/r/MachineLearning
https://redd.it/100amit
A slightly modified version of my primality checking algorithm plotted over polar coordinates.
/r/mathpics
https://redd.it/xaij03
Element N14 ( by ojovivoMotion )
/r/mathpics
https://redd.it/xm1415
Base 3, diagonal elementary CA, made in MS Excel and colorized with Photoshop. I wanna get into making prints. (LIC)
/r/mathpics
https://redd.it/z36z3w
Hilbert Curve, part II
/r/mathpics
https://redd.it/yz9kzy
Simulations of Effect of Detonation Upon Plate
/r/mathpics
https://redd.it/zq3ig8
XVII Circulo circum consumitur | 03-10-19 | by Xponentialdesign
/r/mathpics
https://redd.it/zu37xr
Please suggest some cool mathematical models that I can 3d print.
I will be getting a 3d printer in the near future. What are some cool and crazy mathematical models that I could print. Link to an .stl file will be great, but I am also looking forward to creating my own models so links to formulas and pictures also appreciated.
I am going to start with platonic solids (nested wireframes maybe), fractal solids (Sierpiński pyramid, mandelbulb), Gömböc, Klein bottle, slide rule etc. But I am looking for more ideas.
Editing to add more ideas: solids of constant width, sphericons
/r/mathpics
https://redd.it/zw64c4
[OC] Most Popular Movie Genre Combinations up to 2023
/r/dataisbeautiful
https://redd.it/100il3k
Manhattan Neighborhoods by Number of Trees (excluding Central Park) [OC]
/r/dataisbeautiful
https://redd.it/100fkei
2022 in Search Trends [OC]
/r/DataArt
https://redd.it/zv2jba
Recommendations for measure theory/measure theoretic probability books Q
Hi, I’m an undergrad intending on pursuing a PhD in stats. Background of real analysis at the level of Stephen abbot understanding analysis and baby rudin. What’s a good book on measure theoretic probability or measure theory which could be a good taste of something I’d see in a phd program in statistics?
/r/statistics
https://redd.it/zzfi85
D How popular is SAS compared to R and Python?
/r/statistics
https://redd.it/1003gwv
Which country has the least Attractive People according to Europe?
/r/MapPorn
https://redd.it/100a6gn
D Data cleaning techniques for PDF documents with semantically meaningful parts
I am seeking insights and best practices for data preprocessing and cleaning in PDF documents. I am interested in extracting only the body text content from a PDF and discarding everything else, such as page numbers, footnotes, headers, and footers (see attached image for an example of semantically meaningful sections).
I have noticed that in Microsoft Word, a user can simply drag in a PDF and Word seems to automatically understand which parts are headers, footnotes, etc. I am speculating that Word may be utilizing machine learning techniques to analyze the layout and formatting of the PDF and classify different sections accordingly. Alternatively, Word may be utilizing pre-defined rules or patterns to identify common elements such as headers and footnotes. I know of related techniques for example to extract layout information from receipts and the like (LayoutLM, Xu et al., https://arxiv.org/abs/1912.13318) and tabular data (TableNet, Paliwal et al., https://ieeexplore.ieee.org/document/8978013), but nothing to solve layout extraction in this particular domain.
I am curious to know if there are any techniques or algorithms that can replicate this behavior in Word. Any suggestions or recommendations for data cleaning in PDF documents, would be greatly appreciated.
Image of PDF with semantically meaningful sections
/r/MachineLearning
https://redd.it/100rbhp
□ 6 □ Carbon | 07-04-18 | by Xponentialdesign
/r/mathpics
https://redd.it/xbf2dg
Animated version of the bowtiscate from my previous post
/r/mathpics
https://redd.it/yxw6h9
Clock with only 9s
/r/mathpics
https://redd.it/yz2smk
A Spherical triangle and a Cardioid on a surface of a unit Sphere
https://youtu.be/bwPWsahg-cs
/r/mathpics
https://redd.it/zbpwg9
Non-Euclidian Cone (Complex Set)
/r/mathpics
https://redd.it/zhh94j
Truncated Octahedron 3D model I made from scratch (explanation in a comment)
/r/mathpics
https://redd.it/zuljxi
Doomslayer VS Topology
/r/mathpics
https://redd.it/zyztx3
[OC] Average IMDB rating of US feature films. Has the quality gone down?
/r/dataisbeautiful
https://redd.it/100lknx
Dataset for football (soccer) line-ups
Does anyone know if there's a free database with the line-ups from the premier league/top 5 league competitions post-2016? I saw there was an old post with one but that database is old/discontinued. I have tried to use some betting UIs but I don't get how to use them. Alternatively, I can manually collect or try to scrape the data but I'm not too experienced with coding so any tips would be massively appreciated!
/r/datasets
https://redd.it/zz0y1j
PyIceberg 0.2.0
https://redd.it/zhfa7a
@datascientology
E An Interactive Introduction to Statistics
I am a statistics major and I have written a beginners guide to statistics, it features many interactive visualizations and I have focussed on getting the key ideas across more than stressing the theoretical details.
Baida - Statistics
The guide is already in a polished state, but I'm anyways looking for feedback to improve the clarity of the explanations and suggestions on which topics to cover next.
/r/statistics
https://redd.it/zzqtzf
interesting porn parameters
https://redd.it/100909m
@datascientology
[D] Is there any research into using neural networks to discover classical algorithms?
Correct me if any of these priors are wrong:
* Every problem solvable by a neural network is provably solvable in code, although not necessarily in a useful way - at worst you could generate the pytorch source code and the model weights.
* Neural networks can discover algorithms during training, and use them internally to accomplish the task. This happens emergently in today's large transformer models; it's part of learning how to solve the problem.
* While neural networks can do a lot of things that classical algorithms can't, there's also a lot of things that *both* can do - pathfinding for example. Maybe there's more yet-unknown overlap between them.
Stripping away the neural network and running the underlying algorithm could be useful, since classical algorithms tend to run much faster and with less memory.
Has there been any research into converting neural networks into code that accomplishes the same thing? My first thought would be to train a network to take another neural network as input and output the corresponding code. You could create a dataset for this by taking various chunks of code and training neural networks to imitate them.
/r/MachineLearning
https://redd.it/1007w5u
