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Get ready to embark on an exciting journey into the realm of AI and discover how machines can learn and make intelligent decisions. Our blog articles are crafted with simplicity and clarity in mind, making complex machine learning concepts easy to understand for everyone. Whether you’re a beginner or an experienced practitioner, we’ve got you covered with informative and insightful content. Explore the fascinating world of algorithms, models, and data as we delve into supervised and unsupervised learning, reinforcement learning, and more. Discover practical applications in various domains like healthcare, finance, and autonomous vehicles. From introductory guides to advanced techniques, we’re here to help you demystify machine learning and unlock its potential. Join us on this journey as we unravel the secrets of machine learning and empower you to build intelligent systems that can analyze data, make predictions, and drive innovation.
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Image by Author | Ideogram # Introduction When you’re new to analyzing with Python, pandas is usually what most analysts learn and use. But Polars has become super popular and is faster and more efficient. Built in Rust, Polars handles data processing tasks that would slow down other tools. It is designed for speed, memory …
Our new method could help mathematicians leverage AI techniques to tackle long-standing challenges in mathematics, physics and engineering. For centuries, mathematicians have developed complex equations to describe the fundamental physics involved in fluid dynamics. These laws govern everything from the swirling vortex of a hurricane to airflow lifting an airplane’s wing. Experts can carefully craft …
Discovering new solutions to century-old problems in fluid dynamics Read More »
language models has made many Natural Processing (NLP) tasks appear effortless. Tools like ChatGPT sometimes generate strikingly good responses, leading even seasoned professionals to wonder if some jobs might be handed over to algorithms sooner rather than later. Yet, as impressive as these models are, they still stumble on tasks requiring precise, domain-specific extraction. Motivation: …
Image by Editor | ChatGPT/font> As large language models (LLMs) become increasingly central to applications such as chatbots, coding assistants, and content generation, the challenge of deploying them continues to grow. Traditional inference systems struggle with memory limits, long input sequences, and latency issues. This is where vLLM comes in. In this article, we’ll …
Acknowledgements We thank the International Collegiate Programming Contest (ICPC) for their support. This project was a large-scale collaboration, and its success is due to the combined efforts of many individuals and teams. Hanzhao (Maggie) Lin led the overall technical direction for Gemini competitive programming and ICPC 2025 efforts, and co-led with Heng-Tze Cheng on the …
systems, understanding user intent is fundamental especially in the customer service domain where I operate. Yet across enterprise teams, intent recognition often happens in silos, each team building bespoke pipelines for different products, from troubleshooting assistants to chatbots and issue triage tools. This redundancy slows innovation and makes scaling a challenge. Spotting a Pattern in …
Image by Ideogram Most of my days as a data scientist look like this: Stakeholder: “Can you tell us how much we made in advertising revenue in the last month and what percentage of that came from search ads?” Me: “Run an SQL query to extract the data and hand it to them.” Stakeholder: …
Here’s How I Built an MCP to Automate My Data Science Job Read More »
This post introduces the emerging field of semantic entity resolution for knowledge graphs, which uses language models to automate the most painful part of building knowledge graphs from text: deduplicating records. Knowledge graphs extracted from text power most autonomous agents, but these contain many duplicates. The work below includes original research, so this post is …
Image by Author | Ideogram # Introduction You know the basics of Python’s standard library. You’ve probably used functions like zip() and groupby() to handle everyday tasks without fuss. But here’s what most developers miss: these same functions can solve surprisingly “uncommon” problems in ways you’ve probably never considered. This article explains some of these …
Uncommon Uses of Common Python Standard Library Functions Read More »
Why do we still wrestle with documents in 2025? in any data-driven organisation, and you’ll encounter a host of PDFs, Word files, PowerPoints, half-scanned images, handwritten notes, and the occasional surprise CSV lurking in a SharePoint folder. Business and data analysts waste hours converting, splitting, and cajoling those formats into something their Python pipelines will accept. …
Docling: The Document Alchemist | Towards Data Science Read More »
