Your Guide to Quality Data Science Products

Convolutional neural networks and deep learning don't have to be difficult subjects. Read this article if topics like input and output channels, kernels, strides, and receptive fields feel a bit fuzzy to you.

Switching from Excel to R Shiny? Learn how to migrate an analytics tool built with Excel and Google Sheets to an interactive Shiny dashboard.

Version 0.4.0 of shiny.semantic includes a CSS grid to a well-known R package. Learn how to make a Shiny App with CSS Grid.

PP-YOLO is a machine learning object detection framework based on the YOLO algorithm. In this article, we’ll explain what PP-YOLO is, why it is an improvement over YOLOv4, and show you how to use PP-YOLO for object detection.

In this video, Appsilon Senior Data Scientist Olga Mierzwa-Sulima explains best practices for data science teams – whether they are working in the office together or fully remote.

Appsilon has created a package to speed up R Shiny by offloading long-running calculations and unfreezing the Shiny UI.

This article will show you how to speed up R Shiny by pushing actions to the browser. You will learn how to omit the server bottleneck with updateInput, leverage CSS classes, and take advantage of JavaScript actions for smoother Shiny performance.

Recent research on weight poisoning attacks on pre-trained models in the area of NLP may transfer to the context of Computer Vision, where the usage of pre-trained weights is widespread.

Learn best practices for setting up a data science team and kicking off a data analytics project – remote or otherwise. I'll cover Scrum methodology, Asana, GitHub, Docker, renv, linter, and a variety of other tools and workflows.

Appsilon Data Science and RStudio will be hosting a joint webinar on Tuesday, July 28th at 11AM EST. The theme is "Enabling Remote Data Science Teams."

Appsilon engineer Pedro Silva has been named a Grand Prize Winner in RStudio's 2nd Annual Shiny Contest. He created a fully functional video game using Shiny.

xspliner is an R package that helps explain black box ML models. In this presentation, you will learn what PDP curves and GLMs are and how you can calculate them based on black box models. I'll then show you a specific use-case for xspliner.