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 tip of the yr only a few weeks away, neither our authors nor our readers are exhibiting any indicators of slowing down.
We’re thrilled to have printed a few of our strongest articles of the yr up to now month: sensible guides on LLM workflows and assets on profession progress, Python-focused tutorials, and deep dives on not too long ago launched instruments, amongst different standout subjects. 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 varieties of RAG methods by pitting GraphRAG and SQL RAG in opposition to one another, utilizing the identical dataset and questions.
LLM-Powered Time-Sequence Evaluation
Within the second a part of Sara Nobrega’s common collection, we study concerning the prompts we want for superior mannequin growth (suppose ARIMA and LSTM).
Tips on how to 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 lots of different subjects — each evergreen and leading edge.
NumPy for Absolute Learners: A Undertaking-Primarily based Method to Information Evaluation, by Ibrahim Salami
Understanding Convolutional Neural Networks (CNNs) By Excel, by Angela Shi
Run Python As much as 150× Sooner with C, by Thomas Reid
Tips on how to Construct an Over-Engineered Retrieval System, by Ida Silfverskiöld
Constructing a Multimodal RAG That Responds with Textual content, Pictures, and Tables from Sources, by Partha Sarkar
Why I’m Making the Swap to marimo Notebooks, by Parul Pandey
Your Subsequent ‘Massive’ Language Mannequin Would possibly Not Be Massive After All, by Moulik Gupta
In Case You Missed It: Our Newest Creator Q&As
We love sharing our authors’ experience, profession insights, and views on the current developments on the planet of information science and AI. Listed below are our most up-to-date Creator Spotlights.
- “Techniques pondering helps me put the large image entrance and heart”
Shuai Guo on deep analysis brokers, analytical AI vs LLM-based brokers, and methods pondering.
- “The success of an AI product depends upon how intuitively customers can work together with its capabilities”
Janna Lipenkova on AI technique, AI merchandise, and the way area information can change your entire form of an AI answer.
Meet Our New Authors
We hope you are taking the time to discover the superb work from the most recent cohort of TDS contributors:
- Jure Leskovec, a Stanford professor of laptop science and entrepreneur, explains why LLMs aren’t a one-size-fits-all answer for corporations.
- Sherin Sunny, a senior engineer at Walmart, walked us by way of 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 not too long ago written an attention-grabbing mission walkthrough, tutorial, or theoretical reflection on any of our core subjects, why not share it with us?
We’d Love Your Suggestions, Authors!
Are you an current TDS writer? We invite you to fill out a 5-minute survey so we are able to enhance the publishing course of for all contributors.
