Towards Data Science @TDataScience
The world's leading publication for data science and artificial intelligence professionals. Submit an Article ✍️ https://t.co/57pIMegK1o towardsdatascience.com We're Global 🌏 Joined October 2016-
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RAG isn’t dead... but it is evolving. @EivindKjos explores advanced strategies to boost quality, reduce token bloat, and refine retrieval in question-answering systems. towardsdatascience.com/how-to-context…
Tired of theoretical SQL? Our premium course delivers practical coding exercises based on real-world scenarios, with an AI instructor guiding you every step of the way! Go from beginner to expert, querying like a pro. Start your SQL mastery 👉 roadmap.sh/courses/sql
Remember when Heroku’s free tier made sharing ML apps a breeze? That era’s gone — but @huggingface Spaces just might be the next best thing. Ivo Bernardo walks you through how to deploy your own interactive app in minutes. towardsdatascience.com/showcasing-you…
LLMs don’t fail in the lab... they fail in production. This new article by Erika G. Gonçalves walks you through what really breaks in AI apps: silent retrieval failures, prompt drift, hallucinations, biased outputs + more. towardsdatascience.com/ai-operations-…
Do you wonder how leading AI companies manage their LLM context for question answering? In my latest @TDataScience article, I cover: 🔹 Smarter RAG 🔹 Agentic search 🔹 Context engineering for QA systems Read here 👇 towardsdatascience.com/how-to-context…
Your Poisson model might be lying to you. Arnaud Capitaine explores zero-inflated data through a real-world example. Learn why standard GLMs break down, and how ZIP and hurdle models offer a better fit. towardsdatascience.com/zero-inflated-…
Tool definitions = prompts. So why aren’t we prompt-engineering them? Frank Wittkampf's new article shows how tool masking reshapes API interactions for agents. towardsdatascience.com/tool-masking-t…
In his latest career-focused guide, Egor Howell walks us through the books, courses, and resources that helped him the most on his journey to becoming a machine learning engineer. towardsdatascience.com/everything-i-s…
NEW ROADMAP 🚩 Ready to build modern web apps with the power of React? Our free @nextjs learning roadmap is now live! We've laid out everything you need to know, from foundational concepts to server-side rendering and static site generation. roadmap.sh/nextjs
There's very little doubt that agentic AI is reshaping tech careers already. @rpdesai24 explains how data scientists (and tech professionals more broadly) can prepare for a new age of work. towardsdatascience.com/getting-ai-rea…
Can a lightweight logic algorithm outperform today’s most popular LLMs? In Nicolas Garcia Aramouni's new article, he pits GPT-4o, @GeminiApp, and LLaMA 4 against a custom-built Wordle solver. towardsdatascience.com/should-we-use-…
Your Random Forest is a black box. @jamesdeluk's new article uses visualizations of individual predictions and errors to demystify how a random forest works. towardsdatascience.com/a-visual-guide…
Don't miss @taupirho's new article on Google's URL Context Grounding, showing how to use this new tool to extract accurate insights from web content (without the need for RAG pipelines). towardsdatascience.com/googles-url-co…
Build smarter ML pipelines that handle data sparsity. Hugo Ducruc teaches you how transfer learning and shared embeddings could be used to solve real-world problems with sparse data. towardsdatascience.com/what-if-i-had-…
Can messy clinical notes be turned into structured entities? Parul Pandey breaks down how LangExtract uses LLMs. Both proprietary and local. To extract data using schemas and few-shot examples, making unstructured text usable and reliable. towardsdatascience.com/can-langextrac…
What's the difference between prompting/reprompting and constrained decoding for LLMs? @IbrahimHabibEg clarifies the trade-offs in reliability, cost, and flexibility, helping you choose the right technique for your structured output needs. towardsdatascience.com/generating-str…
Where did GPT models emerge from? What made them possible? Rohit Pandey digs into the papers that paved the road for the powerful models we use today. towardsdatascience.com/a-brief-histor…
What is stationarity and how does it play into the process of time series forecasting? Nikhil Dasari presents a beginner-friendly guide to an essential concept. towardsdatascience.com/time-series-fo…
The myth of CNN's massive parameter count is holding you back. Muhammad Ardi's new article reveals how MobileNetV1's core innovation makes it possible to build powerful models for computer vision on the edge. towardsdatascience.com/the-tiny-giant…
Your code for home automation is coupled with a specific device API. Benjamin Lee's new article reveals a better way... building a dedicated MCP server to abstract away device-specific logic. towardsdatascience.com/using-langgrap…
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