All.

Uso #python #rstats & #linux para datear, colecciono viniles, amo: las mates; escuchar wave y pop oscuro; la ciencia ficción; jugar wh4k y leer a Kierkegaard

[All. @ilan2884]

Let's all meet up in the year 2000 #pulp

[Valeriy M., PhD, MBA, CQF @predict_addict]

Did you know that people tried to prove central limit theorem for over two centuries, first starting with de Moivre (1733), then almost a century after by Laplace who both used binomial distribution.

Then it was Poisson who worked on this theorem, and Chebyshev (1890–1891) who gave a rigorous demonstration of it in the middle of the nineteenth century.

At the beginning of the twentieth century, the Russian mathematician Liapounov, Aleksandr Mikhailovich (1901) created the generally recognized form of the central limit theorem by introducing its characteristic functions.

 Markov, Andrei Andreevich(1908) also worked on it and was the first to generalize the theorem to the case of independent variables.

In 1924 Kolmogorov started to become interested in research in Probability Theory and in 1928 he was able for the first time to formulate necessary and sufficient conditions of the Law of Large Numbers that escaped other best mathematicians of the time for many decades.

It has taken the best mathematicians almost two centuries to prove conditions for LLN and prove CLT.

In fact there is almost 500 (!) pages book describing the history of CLT.

https://t.co/UcjmVxs9f2

#statistics #machinelearning #gaussian

[Akshay 🚀 @akshay_pachaar]

ML models can only be as good as the data they are trained on.

Key aspects of data-centric AI:

- Data cleaning
- Handle outliers
- Account for annotator-quality
- Active learning

Folks at MIT have open-sourced a python library that does this all for you 🚀

Read more 🧵👇

[nana🦄 @ds_nana_]

A Math Lover’s Guide to Hidden Markov Models https://t.co/2vWAQf4me9

[Caitlin Hudon @beeonaposy]

Me when the massive dataset I've been building all week finally comes together

[nixCraft 🐧 @nixcraft]

Happy cake day to Linus Torvalds. Thanks for git and Linux kernel and other stuff. Hat tip https://t.co/8d9IYYoZmx

[Abacus.AI @abacusai]

Scikit-learn is one of the most useful and important Python 🐍 libraries for machine learning.

A great cheat sheet that will help you decide which algorithm to use:

[andrej @reactive_dude]

How to write clean code:

"Always code as if the guy who ends up maintaining your code will be a violent psychopath who knows where you live."

― John Woods

[Programmer Humor @PR0GRAMMERHUM0R]

36 different kinds of programmers https://t.co/BJPw08cgtH

[All. @ilan2884]

@marktenenholtz I see tiktok videos while my ML models are training and You all guys should do the same hahaha

[All. @ilan2884]

Learn MLOps in a free online course from @DataTalksClub!

- Processes
- Training models
- Serving models
- Monitoring
- Best practices

More info here: https://t.co/4BsQpAgABb

[All. @ilan2884]

@chesar_che @tunguz Too unfair LGBM si not on the chart, it is almost always faster than XGBM

[Pau Labarta Bajo @paulabartabajo_]

Developing an ML model is the easy part...
Keeping it healthy on production is the challenge.

Wanna get biweekly articles and tips on real-world ML?
👉🏽 https://t.co/97E4zKMjQM

#ml #machinelearning #MLOps

[All. @ilan2884]

@CSProfKGD It was thanks to professor Gil Strang that I fell in love with Linear Algebra

[Kosta Derpanis (sabbatical in Zurich) @CSProfKGD]

Gilbert Strang’s lecture videos on “Matrix Methods in Data Analysis, Signal Processing, and #MachineLearning”

https://t.co/EWY08djuYO

[All. @ilan2884]

@jjsantoso Hola @jjsantoso duda, que ventajas hay de trabajar así en lugar de usar geopandas?

[All. @ilan2884]

@ClementDelangue MLOps totally applied

[All. @ilan2884]

@capeandcode Nice functions! I would like to mention .assign() for creating new columns, ir totally rocks!

[nixCraft 🐧 @nixcraft]

RT if you love Bash.