May Have to Read: Mathematics of Machine Learning Engineers, LLMs, Agent Protocols, etc.
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We are closing another month of multiple months where we have published dozens of new articles on cutting-edge and evergreen topics: from math for machine learning engineers to the internal operation of model context protocols.
Keep reading to explore our most read stories in May – the most useful, useful and thought-provoking articles found in our community.
If you feel like you’ve inspired your passion project or recent discoveries, feel free to share your work with us: We are always open for new author submissions, and our author payment plan has become much streamlined this month.
How to learn the math required for machine learning
Everyone loves a good roadmap. Example: Egor Howell’s viable guide for ML practitioners, outlining the best ways and resources to master the baseline knowledge they need in linear algebra, statistics, and calculus.
Newbie of LLM? Start here
We are excited to release another great guide this month: Alessandra Costa beginners introduce rags, fine tuners, agents and more.
Inheritance: Software Engineering Concept Data Scientists Must Know Success
Still focusing on core skills, Benjamin Lee shares a comprehensive introduction to inheritance, a basic coding concept.
Others may be highlighted
Explore our most popular and widely circulated articles over the past month covering various topical predictions for data engineering, healthcare data and time series:
- Sandi Besen introduces us to the Agent Communications Protocol, an innovative framework that enables AI agents to collaborate “cross teams, frameworks, technologies and organizations.”
- Hailey Quach sticks to the ever-evolving theme and will Very To learn more about MCP (Model Context Protocol) for anyone’s convenience resources.
- How should you implement multiple linear regression analyses on the actual data? Junior Jumbong took us through the process in the patient tutorial.
- Learn how machine learning libraries speed up non-ML computing: Thomas Reid opens up some lesser-known (but very powerful) use cases for Pytorch.
- In one of the best deep dives last month, Yagmur Gulec led us through a preventive health care program that leverages machine learning methods.
- From simple averages to mixed strategies, the latest issue of the Nikhil Dasari series highlights how you can customize model benchmarks for time series predictions.
Meet our new author
Every month, we are excited to welcome brand new data science, machine learning and AI experts. Don’t miss out on the work of some of our latest contributors:
- Florida-based AI researcher Mehdi Yazdani shares his latest work on training neural networks with two targets.
- Joshua Nishanth A joins the TDS community with extensive experience in data science, deep learning and engineering.
We love articles from new authors, so if you recently wrote an interesting project walkthrough, tutorial or theoretical reflection, then why not share any of our core topics with us?
