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@CaeterisAI/ML Engineer. Marathonist. Problem solver. Statistical Learning theorist. Cryptoanalist. Live long and prosper 🖖🏻
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10 Brilliant Mathematics Lectures (free) that you must watch. A Thread 👇
The single biggest argument about statistics: is probability frequentist or Bayesian? It's neither, and I'll explain why. Buckle up. Deep-dive explanation incoming.
Can construct a fixed point M for a lambda term L by defining M = (λx.L(xx))(λx.L(xx)).
A microfluidic device containing 300k networking neurons in 2 isolated populations. Both sides were treated with a unique virus and bridged by axons. Credits: Esmeralda Paric & Holly Stefen #neuroscience #MedTwitter
Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras buff.ly/3zSdTr6
The Danger of Making Decisions based upon Averages: bit.ly/2S3eK5O by @schmarzo ➕ See the book "The Flaw of Averages: amzn.to/3C2CCtY ——— #abdsc #StatisticalLiteracy #MachineLearning #AI #BigData #Analytics #DataScience #Statistics #DecisionScience #DataLiteracy
SHAP: Explain Any Machine Learning Model in Python towardsdatascience.com/shap-explain-a…
Fascinating reading on #GameTheory: amzn.to/2T70A1y "A Nontechnical Intro to the Analysis of Strategy" (3rd Ed.), covers N-person strategies, Nash Equilibria, dominant strategies, #BehavioralEconomics, #ExperimentalEconomics, auctions, bargaining, etc. ——— #Gamification
bayesian-machine-learning: Notebooks about Bayesian methods for machine learning Lang: Jupyter Notebook ⭐️ 1507 #MachineLearning github.com/krasserm/bayes…
No es su DNA, es su maravillosa forma de expresarlo. 😍🧬💕
Muchos modelos de simulación dan por hecho a Brasil cómo campeón, olvidan que la Argentina apela a su leyenda cuando tiene a la canarinha enfrente.
I'm teaching Computational Learning Theory based out of Shalev and Ben David's classic text 'Understanding Machine Learning: From theory to Algorithms'. In response to multiple requests, I've decided to make class/lecture notes open to public.
After 8 years working as a data science consultant for @IBM , I learned a lot and I want to share with you: My 3 simple steps recipe to become a data superhero🦸♀️🦸🦸♂️: All 3 steps below in the thread 👇
En mi opinión una de las ecuaciones mas bellas de la física. Tan intuitivamente fácil de entender y obtener y tan endemoniadamente difícil de resolver en general de manera analítica. Navier-Stokes
The Navier-Stokes Equations 💦 In 1845, Sir George Stokes had derived the equation of motion of a viscous flow by adding Newtonian viscous terms, thereby the NSEs had been brought to their final form which has been used to generate numerical solutions for fluid flow ever since.
I love you so much. You are such an inspiration. This is from the first time I heard you speak. I will always take notes 😉
At 2100+ (not a typo) pages 🤯, this is almost all the Math you need for Machine Learning without dumbing it down! PDF link: cis.upenn.edu/~jean/math-dee…
Richard Feynman sometimes used to practice for five hours in an empty classroom before delivering his lecture. It's true that when passion and hard work combine, it's a masterpiece.
Quizás, si el azar, si las ganas, si el amor. Quizás seamos dos gotas de aquel río que nunca vuelve a ser. Quizás en la metamorfosis perdure algo en nosotros, inmóvil. Algo que encontró lo que buscaba.
This just arrived in today’s mail, and it looks very exciting! In the next two tweets in this thread, I’ll show you a sample of the table of contents, and an excerpt. Enjoy!
Getting started with machine learning interpretability can be intimidating. Especially if you start with Shapley values & co. For an intuitive and easy start, I recommend these 3 methods: • ICE curves • Counterfactual explanations • Permutation importance
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