By no means miss a brand new version of The Variable, our weekly e-newsletter that includes a top-notch number of editors’ picks, deep dives, neighborhood information, and extra.
With the top of the yr only a few weeks away, neither our authors nor our readers are displaying any indicators of slowing down.
We’re thrilled to have revealed a few of our strongest articles of the yr up to now month: sensible guides on LLM workflows and assets on profession development, Python-focused tutorials, and deep dives on just lately launched instruments, amongst different standout matters. Learn on to meet up with (or revisit) November’s most-read tales.
Graph RAG vs SQL RAG
Which database paradigm delivers extra correct and insightful outcomes? Reinhard Sellmair units out to evaluate the efficiency of two forms of RAG techniques by pitting GraphRAG and SQL RAG towards one another, utilizing the identical dataset and questions.
LLM-Powered Time-Sequence Evaluation
Within the second a part of Sara Nobrega’s standard sequence, we be taught concerning the prompts we want for superior mannequin improvement (suppose ARIMA and LSTM).
How you can Construct Machine Studying Tasks That Assist You Get Employed
Not all ML portfolios are created equal. Egor Howell shares time-tested insights on what works — and what doesn’t.
Different November Highlights
Don’t miss our different high reads from the previous month, tackling NumPy, Multimodal RAG, marimo notebooks, and plenty of different matters — each evergreen and leading edge.
NumPy for Absolute Rookies: A Mission-Primarily based Method to Information Evaluation, by Ibrahim Salami
Understanding Convolutional Neural Networks (CNNs) By way of Excel, by Angela Shi
Run Python As much as 150× Quicker with C, by Thomas Reid
How you can Construct an Over-Engineered Retrieval System, by Ida Silfverskiöld
Constructing a Multimodal RAG That Responds with Textual content, Photos, and Tables from Sources, by Partha Sarkar
Why I’m Making the Swap to marimo Notebooks, by Parul Pandey
Your Subsequent ‘Giant’ Language Mannequin Would possibly Not Be Giant After All, by Moulik Gupta
In Case You Missed It: Our Newest Writer Q&As
We love sharing our authors’ experience, profession insights, and views on the current developments on this planet of information science and AI. Listed below are our most up-to-date Writer Spotlights.
- “Methods considering helps me put the large image entrance and middle”
Shuai Guo on deep analysis brokers, analytical AI vs LLM-based brokers, and techniques considering.
- “The success of an AI product is dependent upon how intuitively customers can work together with its capabilities”
Janna Lipenkova on AI technique, AI merchandise, and the way area information can change all the form of an AI resolution.
Meet Our New Authors
We hope you are taking the time to discover the superb work from the newest cohort of TDS contributors:
- Jure Leskovec, a Stanford professor of pc science and entrepreneur, explains why LLMs aren’t a one-size-fits-all resolution for corporations.
- Sherin Sunny, a senior engineer at Walmart, walked us via the creation of a pc imaginative and prescient mission aimed toward detecting leaves.
- Manuel Franco de la Peña launched us to ShaTS, a novel Shapley-based explainability methodology particularly designed for time-series fashions, which he co-created.
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 matters, why not share it with us?
We’d Love Your Suggestions, Authors!
Are you an present TDS creator? We invite you to fill out a 5-minute survey so we will enhance the publishing course of for all contributors.
