The Art of Spending: Optimizing Your Marketing Budget with Nonlinear Optimization by Maxime Labonne buff.ly/424ID46

A Performant Way to Query Streaming Data by Harrison Hoffman buff.ly/3Oy3l9B

Running with Pandas AI: an Exploration of the Boston Marathon by Sejal Dua buff.ly/3MPcaKI

From Data Engineering to Prompt Engineering by Christian Koch, Markus Stadi, and Lukas Berle buff.ly/43lIV7A

5 Data Science Portfolio Mistakes by Shaw Talebi buff.ly/3BD6vRI

RT @_March08_: Data represented in tabular form (such as CSV files) can be distinguished into row-major format and column-major format. Depending on the paradigm there are best practices to optimize file read and write times. Check out my Medium artic… x.com/TDataScience/s…

RT Woojin Kim: The article explains how generative language models, particularly GPT models, work, including the history of generative language models—good, clear explanations. buff.ly/3BMRZGQ #GenerativeAI #GenAI #LLM #ChatGPT #GPT4

Continuing his exploration of physics-informed neural networks (PINN), Shuai Guo exmaines how you can enhance their stability through ensemble learning and dynamic solution interval expansion. buff.ly/42OhVOm

Niklas Lang's new explainer walks us through the role of the dropout layer in the context of improving the training of neural networks and reducing overfitting. buff.ly/3BG4K62

If you're a data scientist working with marketing and sales teams, don't miss Salvatore Cagliari's tutorial on determining your advertising ROI over time. buff.ly/3MnJLdb

How do you prevent data leakage from hurting the performance of your machine learning project? Andreas Lukita's new post provides a clear, structured approach. buff.ly/45hiu4U

👨‍💻La Herramienta Digital Avanzada para la Gestión Eficiente de las Marcas✅ #BrandCenter #Aggylon #MarketingDigital #BlogsterApp blgs.co/QONjNN

If you're a Magic: the Gathering Arena player who also happens to have a strong interest in probability and statistics, Edward Krueger and Erin Oefelein's new post would be right up your alley. buff.ly/3Wl54Rg

Interested in causal inference modeling? Matt offers a comprehensive review of different approaches to using propensity scores. buff.ly/3BG5nNd

For a one-stop resource on detecting data drift with hypothesis testing, bookmark Michał Oleszak's clear, easy-to-follow guide. buff.ly/3BCSVxE

How do interaction terms work in linear regression and why do they matter? Eryk Lewinson's latest tutorial has the answers. buff.ly/3MgTvWL

🚀¿Sabes que Beneficios tiene Automatizar tu Marketing?🎯 #Socialmedia #emailMarketing #ecommerce #MarketingDigital #BlogsterApp blgs.co/16DJ83

"After reading a good number of articles, books, and guides, I realized that most lack clear and intuitive explanations of how logistic regression works." Igor Šegota brings clarity to a common, but often-misunderstood model. buff.ly/45fwV9F

4 Ways to Get the Most Out of Your Data Science Degree by Matt Chapman buff.ly/3MQJuRD

Improve your Gradient Descent: The Epic Quest for the Optimal Stride by Naman Agrawal buff.ly/3WvW8IN