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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 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 »
Image by Author | Canva & ChatGPT # Introduction GitHub has become the go-to platform for beginners eager to learn new programming languages, concepts, and skills. With the growing interest in agentic AI, the platform is increasingly showcasing real projects that focus on “agentic workflows,” making it an ideal environment to learn and build. One …
Federated Learning (FL) is we train AI models. Instead of sending all your sensitive data to a central location, FL keeps the data where it is, and only shares model updates. This preserves privacy and enables AI to run closer to where the data is generated. However, with computation and data spread across many devices, …
Fighting Back Against Attacks in Federated Learning Read More »
Image by Author | Ideogram # Introduction You just pushed your Python app to production, and suddenly everything breaks. The app worked perfectly on your laptop, passed all tests in CI, but now it’s throwing mysterious import errors in production. Sound familiar? Or maybe you’re onboarding a new developer who spends three days just trying …
A Gentle Introduction to Docker for Python Developers Read More »
