[ad_1] How can we think about thinking in the simplest way possible? Opening Pandora’s box (image by author)In the 17th century, René Descartes introduced a relatively new idea — the dictum “cogito ergo sum” (“I think, therefore I am”). This simple formulation served as the basis of Western philosophy and defined for centuries our ideas…

[ad_1] How cloud computing and analytics engineering forced the transition from ETL to ELT Image generated via DALL-EETL (Extract-Transform-Load) and ELT (Extract-Load-Transform) are two terms commonly used in the realm of Data Engineering and more specifically in the context of data ingestion and transformation. While these terms are often used interchangeably, they refer to slightly…

[ad_1] How to improve the performance of your Retrieval-Augmented Generation (RAG) pipeline with these “hyperparameters” and tuning strategies Tuning Strategies for Retrieval-Augmented Generation ApplicationsD ata Science is an experimental science. It starts with the “No Free Lunch Theorem,” which states that there is no one-size-fits-all algorithm that works best for every problem. And it results…

[ad_1] Experimenting with Large Language Models for free Artistic representation of the LangChain, Photo by Ruan Richard Rodrigues, UnsplashEverybody knows that large language models are, by definition, large. And even not so long ago, they were available only for high-end hardware owners, or at least for people who paid for cloud access or even every…

[ad_1] In 3 words: timeliness, methodology, and digestibility A couple of weeks ago, I wrote about building systems to generate more quality insights. I presented how you could increase the output of your team by working on areas such as processes, tools, culture, etc., but I never defined what I meant by “quality” — so…

[ad_1] A Glossary with Use Cases for First-Timers in Data Engineering An happy Data Engineer at workAre you a data engineering rookie interested in knowing more about modern data infrastructures? I bet you are, this article is for you! In this guide Data Engineering meets Formula 1. But, we’ll keep it simple. I strongly believe…

[ad_1] Graph neural networks (GNNs) and large language models (LLMs) have emerged as two major branches of artificial intelligence, achieving immense success in learning from graph-structured and natural language data respectively. As graph-structured and natural language data become increasingly interconnected in real-world applications, there is a growing need for artificial intelligence systems that can perform…

[ad_1] With A Tail of Cat Food Preferences Photo by Anastasiia Rozumna on UnsplashWelcome to the ‘Courage to learn ML’. This series aims to simplify complex machine learning concepts, presenting them as a relaxed and informative dialogue, much like the engaging style of “The Courage to Be Disliked,” but with a focus on ML. In…

[ad_1] Photo by ThisisEngineering RAEng on UnsplashDescribing the nature with the help of analytical expressions verified through experiments has been a hallmark of the success of science especially in physics from fundamental law of gravitation to quantum mechanics and beyond. As challenges such as climate change, fusion, and computational biology pivot our focus toward more…

[ad_1] Data Science Fundamentals Beginner’s practical guide to discrete optimisation in Python 10 min read · 16 hours ago Data Scientists tackle a wide range of real-life problems using data and various techniques. Mathematical optimisation, a powerful technique that can be applied to a wide range of problems in many domains, makes…