From TF-IDF to Transformers: Implementing Four Generations of Semantic Search

This article covers Python libraries that make large-scale data processing faster, more scalable, and easier to manage across modern data workflows.

What a recent study on ChatGPT, Python, R, and Stata tells us about AI-assisted coding for causal inference The post Can AI Write Your Code? appeared first on Towards Data Science.

Let's take a look at five more fundamental concepts that every Python developer should have in their toolkit.

Simple step-by-step tutorial to building an AI agent in Python The post The Ultimate Beginners’ Guide to Building an AI Agent in Python appeared first on Towards Data Science.

I have been experimenting with the OpenAI Agents SDK, and it has quickly become one of my favorite ways to build agentic AI applications.

turbovec brings Google Research's TurboQuant algorithm to vector search, offering 16x compression and zero codebook training for RAG pipelines. The post Meet Turbovec: A Rust Vector Index with Python Bindings, and Built on Google’s TurboQuant Algorithm appeared first on MarkTechPost.

AI agents can quickly become expensive without a clear strategy for planning, skill coverage, and budgets. This article shows how to use operations research and data science to optimize AI agent cost and resource allocation. You will learn how to frame common agent problems—skill coverage, project assignment, and budgeting—as set covering, assignment, and knapsack optimization models in Python using Gurobi. The post Optimizing AI Agent Planning with Operations Research and Data Science appeared first on Towards Data Science.

Learn how to utilize Python's Mimesis library for anonymizing sensitive production data, based on a step-by-step example to try yourself.