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There’s all the time one thing thrilling and energizing within the air once we flip the calendar to September, and this yr was no exception. Positive, bidding farewell to lengthy sunny days and a barely slower tempo could make anybody a bit wistful, however not for lengthy—not when there’s a lot taking place within the ML and AI scene, so many new instruments and improvements to find out about, and plenty of new expertise to develop.
We’re thrilled to share our most-read and -shared articles of the previous month in case you missed any of them (or simply wish to revisit a favourite or two). Much more than typical, they characterize the total breadth of subjects our authors cowl, from core programming expertise to cutting-edge LLM strategies, so we’re sure that you just’ll discover one thing in our September highlights to pique your curiosity. Completely happy studying, and right here’s to a brand new season filled with studying and progress!
Month-to-month Highlights
- How to Implement Graph RAG Using Knowledge Graphs and Vector Databases
Our prime learn of the month got here from Steve Hedden: a transparent and accessible step-by-step tutorial on implementing retrieval-augmented era (RAG), semantic search, and suggestions. - Data Scientists Can’t Excel in Python Without Mastering These Functions
There’s all the time room for one more strong Python tutorial — and Jiayan Yin’s compendium of key capabilities for knowledge scientists proved particularly useful for our readers. - Python QuickStart for People Learning AI
Extra Python! Shaw Talebi’s beginner-friendly information focuses on the programming subjects you’ll must grasp in case your finish purpose is to develop customized AI tasks and merchandise. - Automated Prompt Engineering: The Definitive Hands-On Guide
Keen on studying methods to automate immediate engineering and unlock vital efficiency enhancements in your LLM workload? Don’t miss Heiko Hotz’s sensible information.
- GenAI with Python: Build Agents from Scratch (Complete Tutorial)
Leveraging the mixed energy of Ollama, LangChain, and LangGraph, Mauro Di Pietro walked us by way of your entire workflow of making customized AI brokers. - SQL: Mastering Data Engineering Essentials (Part I)
Whether or not you’re new to SQL or might use refresher, Leonardo Anello’s complete introduction, aimed particularly at knowledge engineers, is a strong, one-stop useful resource. - Choosing Between LLM Agent Frameworks
What are the tradeoffs between constructing bespoke code-based brokers and counting on the main agent frameworks? Aparna Dhinakaran shares sensible insights and suggestions on a key query. - Analytics Frameworks Every Data Scientist Should Know
Drawing on her earlier expertise as a advisor, Tessa Xie gives knowledge professionals useful tips about “methods to break down an summary enterprise drawback into smaller, clearly outlined analyses.” - Beyond Line and Bar Charts: 7 Less Common But Powerful Visualization Types
From bump charts to round bar plots and Sankey diagrams, Yu Dong invitations us to develop our visual-design vocabulary and experiment with less-common visualization approaches. - 5 Tips To Make Your Resume *Really* Stand Out in FAANG Applications
In a aggressive market, each element counts, and small changes could make a serious distinction—which is why you must discover Khouloud El Alami’s actionable recommendation for present job seekers.
Our newest cohort of latest authors
Each month, we’re thrilled to see a recent group of authors be a part of TDS, every sharing their very own distinctive voice, data, and expertise with our group. In case you’re searching for new writers to discover and comply with, simply browse the work of our newest additions, together with Alexander Polyakov, Harsh Trivedi, Jinhwan Kim, Lenix Carter, Gilad Rubin, Laurin Brechter, Shirley Bao, Ph.D., Iqbal Rahmadhan, Jesse Xia, Sezin Sezgin-Rummelsberger, Reinhard Sellmair, Yasin Yousif, Hui Wen Goh, Amir Taubenfeld, Sébastien Saurin, James Gearheart, Zackary Nay, Jens Linden, PhD, Eyal Kazin, Dan Beltramo, Sabrine Bendimerad, Niklas von Moers, Milan Tamang, Abhinav Prasad Yasaswi, Abhinav Kimothi, Miguel Otero Pedrido, Oliver Ma, Hamza Farooq, Shanmukha Ranganath, Maarten Sukel, Murilo Gustineli, Luiz Venosa, Saankhya Mondal, David Vaughn, Prasad Mahamulkar, Federico Rucci, Philippe Ostiguy, M. Sc., Anurag Bhagat, and Megan Grant, amongst others.
Thanks for supporting the work of our authors! We love publishing articles from new authors, so in the event you’ve just lately written an fascinating mission walkthrough, tutorial, or theoretical reflection on any of our core subjects, don’t hesitate to share it with us.
Till the following Variable,
TDS Staff
Graph RAG, Automated Prompt Engineering, Agent Frameworks, and Other September Must-Reads was initially printed in Towards Data Science on Medium, the place persons are persevering with the dialog by highlighting and responding to this story.