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There’s at all times 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 study, and many 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 need to revisit a favourite or two). Much more than regular, they signify the total breadth of matters our authors cowl, from core programming expertise to cutting-edge LLM strategies, so we’re sure that you simply’ll discover one thing in our September highlights to pique your curiosity. Glad studying, and right here’s to a brand new season stuffed with studying and development!
Month-to-month Highlights
- How one can Implement Graph RAG Utilizing Data 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 proposals. - Knowledge Scientists Can’t Excel in Python With out Mastering These Capabilities
There’s at all times room for one more strong Python tutorial — and Jiayan Yin’s compendium of key features for knowledge scientists proved particularly useful for our readers. - Python QuickStart for Individuals Studying AI
Extra Python! Shaw Talebi’s beginner-friendly information focuses on the programming matters you’ll must grasp in case your finish objective is to develop customized AI initiatives and merchandise. - Automated Immediate Engineering: The Definitive Palms-On Information
Interested by studying how you can automate immediate engineering and unlock important efficiency enhancements in your LLM workload? Don’t miss Heiko Hotz’s sensible information.
- GenAI with Python: Construct Brokers from Scratch (Full Tutorial)
Leveraging the mixed energy of Ollama, LangChain, and LangGraph, Mauro Di Pietro walked us by your entire workflow of making customized AI brokers. - SQL: Mastering Knowledge Engineering Necessities (Half I)
Whether or not you’re new to SQL or may use an excellent refresher, Leonardo Anello’s complete introduction, aimed particularly at knowledge engineers, is a strong, one-stop useful resource. - Selecting Between LLM Agent Frameworks
What are the tradeoffs between constructing bespoke code-based brokers and counting on the foremost agent frameworks? Aparna Dhinakaran shares sensible insights and proposals on a key query. - Analytics Frameworks Each Knowledge Scientist Ought to Know
Drawing on her earlier expertise as a marketing consultant, Tessa Xie gives knowledge professionals useful tips about “how you can break down an summary enterprise drawback into smaller, clearly outlined analyses.” - Past Line and Bar Charts: 7 Much less Widespread However Highly effective Visualization Varieties
From bump charts to round bar plots and Sankey diagrams, Yu Dong invitations us to broaden our visual-design vocabulary and experiment with less-common visualization approaches. - 5 Ideas To Make Your Resume *Actually* Stand Out in FAANG Purposes
In a aggressive market, each element counts, and small changes could make a significant distinction—which is why it’s best to 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 neighborhood. Should you’re on the lookout 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 should you’ve lately written an attention-grabbing undertaking walkthrough, tutorial, or theoretical reflection on any of our core matters, don’t hesitate to share it with us.
Till the following Variable,
TDS Group
Graph RAG, Automated Immediate Engineering, Agent Frameworks, and Different September Should-Reads was initially printed in In direction of Knowledge Science on Medium, the place persons are persevering with the dialog by highlighting and responding to this story.
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TDS Editors
2024-10-03 13:31:47
Source hyperlink:https://towardsdatascience.com/graph-rag-automated-prompt-engineering-agent-frameworks-and-other-september-must-reads-18ae79f105a7?source=rss—-7f60cf5620c9—4