May's Standout Articles in Data Science and Machine Learning

Catch up on the standout articles from May, featuring topics like speeding up Python, building data science portfolios with ChatGPT, and the latest on Kolmogorov-Arnold Networks.

May’s Standout Articles in Data Science and Machine Learning

As May comes to a close and summer approaches, it’s time to reflect on the standout articles from the past month. These stories resonated with learners and practitioners across various data science and machine learning disciplines.

Monthly Highlights

Data science is an eclectic field, and our authors’ diverse interests and experiences shine through in their writing. This month, we saw a range of topics, from speeding up Python to building data science portfolios with ChatGPT.

Data Science

Python, in particular, was a hot topic this month. In Python One Billion Row Challenge — From 10 Minutes to 4 Seconds, we saw how Python can be optimized to tackle massive datasets. Meanwhile, N-BEATS — The First Interpretable Deep Learning Model That Worked for Time Series Forecasting explored the latest advancements in deep learning.

Building a strong online presence is crucial for data scientists, and Build a Data Science Portfolio Website with ChatGPT: Complete Tutorial showed us how to create a portfolio website with the help of generative-AI tools. For those interested in the fundamentals, A Complete Guide to BERT with Code provided an in-depth look at the popular language model.

BERT BERT

In the realm of time series forecasting, PCA & K-Means for Traffic Data in Python demonstrated a practical application of data science techniques. Meanwhile, Why LLMs Are Not Good for Coding — Part II sparked a discussion on the limitations of large language models.

KANs in the Spotlight

Kolmogorov-Arnold Networks (KANs) stole the show this month, with three excellent resources to help you get acquainted with this new type of neural network.

KANs KANs

Kolmogorov-Arnold Networks: The Latest Advance in Neural Networks, Simply Explained provided a clear and accessible primer on KANs. Kolmogorov-Arnold Networks (KANs) for Time Series Forecasting applied KANs to time series forecasting, while Understanding Kolmogorov–Arnold Networks (KAN) offered a more comprehensive walkthrough of the paper.

We’re always excited to welcome new authors to our community. If you’ve written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, don’t hesitate to share it with us.