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Some months, our community seems to be drawn to a very small group of topics: a new model or tool comes out, and everyone's attention is focused on the latest, hottest news. Other times, readers seem to move in dozens of different directions, diving into a wide spectrum of workflows and topics. Last month definitely falls into the latter group, and as we looked at the articles that resonated most with our audience, we were surprised (and impressed!) by their diversity of perspectives and focal points.
We hope you enjoy this selection of some of our most read, shared and discussed posts from April, including a couple of this year's most popular articles to date and several top-notch (and beginner-friendly) explainers.
Monthly highlights
- The mathematics behind neural networks
At this point, few of you need an introduction to Christian LeoGuide series on the essential concepts of machine learning. Perhaps none of these building blocks are more essential than neural networks, of course, so it's no surprise that this deep dive into their underlying mathematics has been such a hit with our readers. - Pandas: from messy to beautiful
It's always a pleasure to see an author's first TDS article strike a chord with a wide audience; This is precisely what happened with Anna Zawadzkais a practical guide to improving your Pandas code, providing practical tips to keep it “clean and foolproof.” - A new correlation coefficient
Nowadays it is not very common to see real advances in statistics, which explains why Tim SumnerThe article about a recent paper, which introduced a “new way of measuring the relationship between two variables just like correlation, except possibly better,” generated a massive response from data professionals.
- How to build an open source local LLM chatbot with RAG
Several months after making their initial impact in ML circles, RAG approaches appear to have lost none of their luster. Dr. Leon EversbergThe tutorial is a good example: it adds a novel solution to a growing list of tools that allow us to “talk” to our PDF documents. - Dive into Transformers at hand
Transformers technical guides and walkthroughs aren't exactly hard to find. what sets Srijanie Dey, PhDFurthermore, the contribution of is its accessibility and clarity, which, together with its well-executed illustrations, made it a particularly strong resource for beginners and visual learners. - From Data Scientist to ML/ai Product Manager
Making a career transition is never a trivial task, especially during a difficult period for job seekers. Anna Via offered a generous dose of inspiration, along with more than a few practical tips and ideas, based on his own successful role change to become a machine learning product manager. - The 4 hats of a well-rounded data scientist
What does it take to become a true full-stack data professional? shaw talebi recently released a series that explores (and answers) this question in detail; This post, the first in the sequence, provides a high-level perspective on the core skills of a data scientist who can “see the big picture and drill down into specific aspects of a project as needed.” - Meet NiceGUI – Your Future Favorite Python UI Library
It's hard to keep track of all the new libraries, packages, and platforms that are announced every day, which is why a detailed, objective, first-hand review can be so helpful. That is precisely what Youness Mansar aims to achieve with its introduction to NiceGUI, an open source Python-based user interface framework. - Linear regressions for causal conclusions
Most of the time, keeping things simple is the key to success. That is a point that Maria Mansurova He comes home again and again in his guide to drawing causal conclusions in the context of product analysis, which eschews sophisticated algorithms and complex equations in favor of tried-and-true linear regressions.
Our latest cohort of new authors
Every month, we're thrilled to see a new group of authors join TDS, each sharing their own unique voice, knowledge, and experience with our community. If you're looking for new writers to explore and follow, simply explore the work of our latest additions, including Thomas Reid, Rechitasingh, Anna Zawadzka, Dr. Christoph Mittendorf, Daniel Manrique-Castano, maximum wolf, Mia Dwyer, Nadav Har-Tuv, roger noble and Martin Chaves, Oliver W. Johnson, Tim Sumner, Jonathan Yahav, Nicolas Lupi, julian yip, Nikola Milosevic (data warrior), Sara Nobrega, Anand Majmudar, Wencong Yang, Shahzeb Naveed, Soyoung L, Kate Minogue, Sean Sheng, Dr. John Loewen, Lukasz Szubelak, Pasquale Antonante, Ph.D., Roshan Santhosh, Runzhong Wang, Leonardo Maldonado, Jiaqi Chen, Tobias Schnabel, Jess.Z, Lucas de Lima Nogueira, Merete Lutz, Eric Boernert, John Mayo-Smith, Hadrien Mariaccia, Gretel Tan, Sami Maameri, Ayoub El Outati, Samvardhan Vishnoi, Hans Christian Ekne, David Kyle, Daniel Pazmiño Vernaza, Vu Trinh, Matthew Trentz, Natasha Stewart, Frida Karvouni, Sunila Gollapudiand Haocheng Biamong others.
