Am Neumarkt 😱

#datascience I ran into this hilarious comment on pie chart in a book called The Grammar of Graphics. “To prevent bias, give the child the knife and someone else the first choice of slices.” 😱😱😱

https://bair.berkeley.edu/blog/2020/12/20/lmmem/

#tools#writing https://www.losethevery.com/ > "Very good english" is not very good english. Lose the very.

#datascience#career#academia > I regret quitting astrophysics https://news.ycombinator.com/item?id=25444069 http://www.marcelhaas.com/index.php/2020/12/16/i-regret-quitting-astrophysics/ me too 😂 though not an astrophysicist, I miss academia too

#datascience#audliolization This is the audiolization of the daily new cases for FR, IT, ES, DE, PL between 2020-08-01 and 2020-12-14. I made an audiolization video two years ago. As I am currently under quarantine and the days are becoming so boring, I started to think about the mapping of data points to different representations. We usually talk about visualization because there are so many elements to be used to represent complicated data. Audiolization, on the other hand, leaves us with very few elements to encode. But it's a lot of fun working with audio. So I wrote a python package to map a pandas dataframe/numpy ndarray to midi representation. Here is the package https://github.com/emptymalei/audiorepr

#fun https://youtu.be/-QiM9NUow3c. Have some fun

#machinelearning

#fun haha no crowd 👍

#tools Space: The Integrated Team Environment https://www.jetbrains.com/space/ Wow, I love jetbrains.

#machinelearning https://arxiv.org/abs/2007.04504 Learning Differential Equations that are Easy to Solve Jacob Kelly, Jesse Bettencourt, Matthew James Johnson, David Duvenaud Differential equations parameterized by neural networks become expensive to solve numerically as training progresses. We propose a remedy that encourages learned dynamics to be easier to solve. Specifically, we introduce a differentiable surrogate for the time cost of standard numerical solvers, using higher-order derivatives of solution trajectories. These derivatives are efficient to compute with Taylor-mode automatic differentiation. Optimizing this additional objective trades model performance against the time cost of solving the learned dynamics. We demonstrate our approach by training substantially faster, while nearly as accurate, models in supervised classification, density estimation, and time-series modelling tasks.

#science The ergodicity problem in economics | Nature Physics https://www.nature.com/articles/s41567-019-0732-0 I read another paper about hot hand/gamblers' fallacy a while ago and the author of that paper took a similar view. Here is the article: Surprised by the Hot Hand Fallacy ? A Truth in the Law of Small Numbers by Miller

#ML https://arxiv.org/abs/2012.04863 Skillearn: Machine Learning Inspired by Humans' Learning Skills Interesting idea. I didn't know interleaving is already being used in ML.