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Hot data science related posts every hour. Chat: https://telegram.me/r_channels
[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
Python package for visualizing 1 Million embeddings of images ?
I want to visualize 1 million embeddings of images I have of 600 different classes. I can reduce the feature size if needed also. (original size of embeddings is 160)
/r/visualization
https://redd.it/zzh53s
[OC] Monthly Enplaned Passengers, Hong Kong International Airport and Indianapolis International Airport, 2019-2022
/r/dataisbeautiful
https://redd.it/zzjuck
The most popular languages learnen on Duolingo per country./-
/r/MapPorn
https://redd.it/zzvr2v
1923-2023 Centennial: pivotal historical events
/r/Infographics
https://redd.it/zzijn9
[OC] Weekly Plastic and Metallic waste from a 150 Seat Diner (lbs)
/r/dataisbeautiful
https://redd.it/zzej0r
How the loose definition of "mass shooting" changes the debate around gun control
/r/Infographics
https://redd.it/zzhu04
[OC] I've tracked my net worth an other relevant financial information for 12 years.
/r/dataisbeautiful
https://redd.it/zzeso1
Heavy Metals perspective: what $1,000 buys you?
/r/Infographics
https://redd.it/zy84ks
What’s your preferred feature selection method?
I understand it’s very data dependent, but what’s your go-to to start analyzing?
I generally try to keep it simple and use Pearson’s correlation for finding linear relationships and mutual information for non-linear relationships. Both are variable specific and don’t apply to a combination of variables.
/r/datascience
https://redd.it/zyh6al
World population 2023 in a single chart calculate in millions of people. China, India, the US, and the EU combined generate half of the world’s GDP and are home to almost half of the world’s population [OC]
/r/dataisbeautiful
https://redd.it/zyzlu7
Even the graphic designers are on strike
/r/dataisugly
https://redd.it/zxhc8r
I thought I could follow it, then I got lost again
/r/dataisugly
https://redd.it/zy8van
The job description of this unpaid internship is insane
https://redd.it/zys7g5
@datascientology
[OC] The World Cup makes people google participating countries like no other event
/r/dataisbeautiful
https://redd.it/zyaiul
swe vs ds
I (29yr) have been managing a farm for 6 years and about to resign. Currently making 150k/year and am ok without income for quite some time. Just feel like I have a lot of skills that I'm not using and ready for a career change. I'm interested in finance, math (extremely good in it and won multiple national math olympics awards) and programming. I was thinking either software engineering or data science could be interesting but open to any other ideas. What would you recommend?
/r/datascience
https://redd.it/1000yz9
Cultural regions map of the contiguous 48 American states. V.5 ( Opinionated, not factual, made with communal input)
/r/MapPorn
https://redd.it/1001cxi
Where rent is most and least affordable in the U.S. (based on percentage total average income).
/r/visualization
https://redd.it/zxpqv0
[OC] Around 30% of countries spend more than 2% of GDP on their military
/r/dataisbeautiful
https://redd.it/zzp93v
State Population Shifts in the US - The COVID Years
/r/MapPorn
https://redd.it/zzc3ci
Alcohol legislation in the United State (2020 data)
/r/MapPorn
https://redd.it/zzazqq
Legality of Islamic Dresses for Woman
/r/MapPorn
https://redd.it/zz4z5s
Contour Map of the Sub Continent [OC]
/r/dataisbeautiful
https://redd.it/zz0muf
U.S. cities with the highest percentages of second homes
/r/Infographics
https://redd.it/zz22c6
Legality of consensual sex between siblings in Europe.
/r/MapPorn
https://redd.it/zyu1b0
Courtesy of FT - why not simply use secondary axis?
/r/dataisugly
https://redd.it/zyj0sh
Laptop for Machine Learning
I am an MS Data Analytics student. During the assignment I received, my current laptop (Specs - i5 10th gen, 8GB RAM, 512 SSD, OS - Windows) faced some problems and took longer to train models. I am thinking of buying a new laptop for the upcoming semester which will include modules like Scalable Machine Learning and Modelling and Simulation of Natural Systems plus my dissertation. I am locked on two choices -
1. MacBook M1 Air with 16GB RAM and 512GB SSD
2. MacBook M2 Pro with 16GB RAM and 256GB SSD
I have mainly chosen apple devices because my other devices (phone and iPad) are from Apple too and would benefit from the Apple ecosystem. Also, the Macs are lighter and have a longer battery life, providing great portability.
Still, if you think I should go with a windows laptop, please suggest that too.
/r/datascience
https://redd.it/zyhtgb
drawing lissajous figures with low sample rates reveals unseen figures
https://imgur.com/a/lsRDBOt
/r/mathpics
https://redd.it/zxmj3h
[OC] Star Wars character screentimes
https://redd.it/zykf6q
@datascientology