This topology made me smile. [bityl.co/KVvn]

⬇️ Highly recommended book available in 🆓 PDF "Tensor Methods in Statistics" by Peter McCullagh (second edition, Dover publication) Demonstrate usefulness of tensors and their notations for cumulants and Edgeworth series in statistics

PyTorch vs TensorFlow: New survey settles the debate. - PyTorch → ~25% faster in training, ~78% faster in eval. Simpler for research. TensorFlow → Stronger in deployment (mobile, browser, server). Read the whole survey here: arxiv.org/pdf/2508.04035

Statistics:

Correlation is the skill that has singlehandedly benefitted me the most in my career. In 3 minutes I'll demolish your confusion (and share strengths and weaknesses you might be missing). Let's go:

Forthcoming book in town! "Mathematical Foundations of Deep Learning Models and Algorithms" HP: mathdl.github.io Publisher: bookstore.ams.org/view?ProductCo…

Looking to visualize set intersections in R? ggupset, an extension of ggplot2, provides a powerful and intuitive way to create UpSet plots, making it easy to uncover patterns and relationships across overlapping sets. ✔️ Effortlessly generate clear and insightful UpSet plots. ✔️

Best Statistics Courses- mltut.com/best-statistic… Kirk Borne Antonio Grasso @ronald_vanloon #MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras

K-means is one of the most powerful algorithms for data scientists. But it's confusing for beginners. Let's fix that:

I struggled with git until I learned these 17 commands:

Statistical Methods: The Geometric Approach link.springer.com/book/10.1007/9…

Mathematical Theory of #DeepLearning [download 282-page PDF]: arxiv.org/abs/2407.18384 + + See also this book: amzn.to/3BqaW5e ————— #AI #DataScience #ML #MachineLearning #Calculus #DataScientist #Mathematics #Algorithms

At MIT, I learned about RNNs in my NLP class with Prof. Michael Collins. He built a model from my keystrokes to predict who I was. To me, it felt like a magic box. Years later, when I had to teach RNNs, I forced myself to go inside the box. ⬇️ Download: byhand.ai/rnn

NN-SVG: Create neural network architecture drawings parametrically! Export them to SVG files and use them in your work!

🧩 [TIP] – Modelado y forecasting reproducible con {fable} ¿Querés crear pronósticos robustos usando workflows tidy? Con {fable} podés acceder a una colección de modelos de forecasting (ETS, ARIMA, SNAIVE y más) que se integran con el ecosistema tidy para evaluar, visualizar y

Andrey Kolmogorov: The Soviet Counterpart to Claude Shannon When we think of information theory, the name that usually comes to mind is Claude Shannon, the American mathematician whose 1948 paper “A Mathematical Theory of Communication” founded the field.

Machine Learning. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode geni.us/ML-Course

"Pen and Paper Exercises in #MachineLearning" Free 211-page PDF: arxiv.org/abs/2206.13446 GitHub: github.com/michaelgutmann ————— #DataScientist #AI #ML #DataScience

🔥 ¿QUÉ MODELO DE REGRESIÓN ELEGIR? GUÍA QUE TE AHORRARÁ HORAS DE DUDAS 🔥 ¿Te suenan a chino LM, GLM, GAMM o GLMM? ¿No sabes qué modelo usar para tu análisis y sientes que entras en un laberinto de términos? 🌀 Aquí te dejo una guía rápida y sencilla para salir de ese bucle.👇🧵

If you are preparing your bachelor statistics course and would like to add optional material for students to better understand statistics on a conceptual level (see topics in the screenshot) my free textbook provides a state of the art overview. lakens.github.io/statistical_in…