After a year of active participation in Kaggle competitions, Geremie Yeo shares key insights and takeaways about the value of taking part — and the strategies that can help you succeed. buff.ly/3wCUtsu

Learn what constitutes a multi-arm bandit problem and how to go about solving one effectively — Jarom Hulet's new deep dive offers a fishing-inspired example to help you intuitively grasp the earn/learn trade-off. buff.ly/44JRN9a

From microwave countdowns to never-ending call holds, Carl Kadie explores different forms of wait times and how they can be represented effectively in Python. buff.ly/4blf2c5

By following along Sasha Korovkina's practical guide, you'll learn how import Python libraries, manipulate and output SQL tables, and more — all within SQL Server. buff.ly/3K1JNH2

The story of a 19th-century shipwreck forms the basis of Statistical's excellent new deep dive on statistical convergence, which covers its mathematical underpinnings in great detail. buff.ly/4bIU2vT

Python Decoration Is Very Useful, But When To Use Them?

Summarised five typical usage patterns of Python decorators by 陶龙泉 buff.ly/4bbM6TV

Kolmogorov-Arnold Networks: the latest advance in Neural Networks, simply explained by Theo Wolf buff.ly/4ak8HMT

'In this article, I’ve gathered recommendations based on the experience I gained in various database-related projects — web applications, data warehouses, scientific projects, etc.' by Krzysztof K. Zdeb buff.ly/4byev6p

N-BEATS — The First Interpretable Deep Learning Model That Worked for Time Series Forecasting by Jonte Dancker buff.ly/3QHIrF1

I just published in Towards Data Science An Intuitive Guide to Docker for Data Science towardsdatascience.com/an-intuitive-g…
#DataScientist #deployment #python #programming

In a detailed, step-by-step tutorial, Benjamin Etienne demonstrates what it takes to build a machine learning pipeline that can actually handle the complexities and curveballs of real-world workflows. buff.ly/3UU5leX

'Early stopping involves monitoring a model’s performance on a validation set during training and stopping the training process once the model’s performance doesn’t improve on this held-out data.'

Harrison Hoffman's new explainer explores early stopping. buff.ly/3QGPayW

In his latest exploration of LLMs' power, Dmitrii Eliuseev tests the ability of open language models to work as a coding assistant, and shares interesting results and insights based on his experiments. buff.ly/3WIVdqy

From preparing your data to building and deploying an API, Joe Sasson walks us through the process of building a RAG system from scratch, with all the necessary code included. buff.ly/3WwRahb

In the latest installment of her Deep Learning Illustrated series, Shreya Rao presents an intuitive, accessible guide to the inner workings of convolutional neural networks. buff.ly/3WBfZbC

'AlphaFold 3 modifies the architecture from AlphaFold 2 to handle a wider range of chemical structures more efficiently, reducing the need for special-case solutions.'

Luciano Abriata looks at the many updates packed into the latest version of the model. buff.ly/4by6xdg

From finding a good mentor (who isn't your manager) to adding depth to your domain knowledge, Haden P offers five actionable tips that can help you not just survive, but thrive during your first year as a data scientist. buff.ly/4aiqRhX

Looking for a substantive programming project to sink your teeth into? How about building your own deep learning framework from scratch with C/C++, CUDA, and Python?

Lucas de Lima Nogueira walks us through the process in detail. buff.ly/4bzHu9V

How can you transform pedestrian dashboards into powerful engines of data storytelling? Michal Szudejko dives deep into the medium's potential for engaging and educating stakeholders. buff.ly/3UK6c0i

In the first part of his Dataform 101 series, Kabeer Akande walks us through the fundamental concepts and terminology of Dataform, a GCP-integrated service for developing and operationalizing complex, SQL-based data pipelines. buff.ly/4bi5TkF