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We appear to be in that candy spot on the calendar between the tip of summer time and the ultimate rush earlier than issues decelerate for the vacation season—in different phrases, it’s the proper time of yr for studying, tinkering, and exploration.
Our most-read articles from October mirror this spirit of centered power, masking a slew of hands-on matters. From actionable AI challenge concepts and information science income streams to accessible guides on time-series evaluation and LLMs, these tales do a fantastic job representing the breadth of our authors’ experience and the variety of their (and our readers’) pursuits. In case you haven’t learn them but, what higher time than now?
Month-to-month Highlights
- 5 AI Projects You Can Build This Weekend (with Python)
In case your sleeves aren’t rolled up simply but, they are going to be shortly: our most-read put up in October, from Shaw Talebi, outlines a number of compelling challenge concepts for anybody who’s been excited about placing their AI data into motion. From resume organizers to a multimodal search instrument, they provide a easy entryway into the ever-expanding world of AI-powered product improvement. - Who Really Owns the Airbnbs You’re Booking? — Marketing Perception vs Data Analytics Reality
In case you’re seeking to sink your enamel into an attention-grabbing data-analysis case research, Anna Gordun Peiro’s newest article matches the invoice. Primarily based on publicly obtainable information, it digs into Airbnb possession patterns, and exhibits readers how they’ll execute the same investigation for the town of their alternative. - LLM Evaluation Skills Are Easy to Pick Up (Yet Costly to Practice)
Creating LLM options requires a serious funding of time and sources, which makes it essential for product managers and ML engineers to get a transparent and correct sense of their efficiency. Thuwarakesh Murallie walks us via the nitty-gritty particulars of leveraging a number of analysis approaches and instruments to attain that often-elusive aim.
- Top 5 Principles for Building User-Friendly Data Tables
“There are quite a few occasions I’m wondering, ‘What does this column imply?’ ‘Why are there two columns with the identical title in desk A and desk B? Which one ought to I exploit?’” Yu Dong introduces 5 helpful guidelines that may guarantee your information tables are accessible, usable, and simply interpretable for teammates and different stakeholders. - How I Studied LLMs in Two Weeks: A Comprehensive Roadmap
Whilst you may suppose that LLMs have been inescapable for the previous couple of years, many practitioners — each new and seasoned — are simply starting to tune in to this buzzing subject; for a structured method to studying all of the fundamentals (after which some), head proper over to Hesam Sheikh’s well-received curriculum. - Understanding LLMs from Scratch Using Middle School Math
In case you may use a extra guided technique to find out about massive language fashions from the bottom up, give Rohit Patel’s debut TDS contribution a attempt: it’s a complete, 40-minute explainer on these fashions’ internal workings—and requires no superior math or machine studying data. - 5 Must-Know Techniques for Mastering Time-Series Analysis
From information splitting and cross-validation to characteristic engineering, Sara Nóbrega’s current deep dive zooms in on the basic workflows you’ll want to grasp to conduct efficient time-series evaluation. - AI Agents: The Intersection of Tool Calling and Reasoning in Generative AI
Few matters in current months have generated as a lot buzz as AI brokers; should you’d wish to deepen your understanding of their potential (and limitations), don’t miss Tula Masterman’s lucid overview, which focuses on how agent reasoning is expressed via instrument calling, explores a number of the challenges brokers face with instrument use, and covers widespread methods to guage their tool-calling capability. - My 7 Sources of Income as a Data Scientist
Most (all?) information professionals know in regards to the perks of working full time at a tech big, however the choices for monetizing your expertise are a lot broader than that. Egor Howell offers a candid breakdown of the assorted income streams he’s cultivated up to now few years since turning into a full-time information scientist.
Our newest cohort of latest authors
Each month, we’re thrilled to see a contemporary 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 in search of new writers to discover and comply with, simply browse the work of our newest additions, together with David Foutch, Robin von Malottki, Ruth Crasto, Stéphane Derosiaux, Rodrigo Nader, Tezan Sahu, Robson Tigre, Charles Ide, Aamir Mushir Khan, Aneesh Naik, Alex Held, caleb lee, Benjamin Bodner, Vignesh Baskaran, Ingo Nowitzky, Trupti Bavalatti, Sarah Lea, Felix Germaine, Marc Polizzi, Aymeric Floyrac, Bárbara A. Cancino, Hattie Biddlecombe, Carlo Peron, Minda Myers, Marc Linder, Akash Mukherjee, Jake Minns, Leandro Magga, Jack Vanlightly, Rohit Patel, Ben Hagag, Lucas See, Max Shap, Fhilipus Mahendra, Prakhar Ganesh, and Maxime Jabarian.
Thanks for supporting the work of our authors! We love publishing articles from new authors, so should you’ve not too long ago written an attention-grabbing challenge walkthrough, tutorial, or theoretical reflection on any of our core matters, don’t hesitate to share it with us.
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LLM Evaluation, AI Side Projects, User-Friendly Data Tables, and Other October Must-Reads was initially printed in Towards Data Science on Medium, the place individuals are persevering with the dialog by highlighting and responding to this story.