Your Guide to Quality Data Science Products

Learn about how shiny.i18n allows you to quickly implement multi-language functionality in Shiny apps and take the user experience to the next level.

Learn about best practices and tools for starting your first deep learning image classification project. This article discusses PyTorch, TensorFlow, fastai, ResNet-50, ResNet-101, MobileNet, and several other concepts and tools.

In this video, Appsilon Co-Founder Damian Rodziewicz explains best practices for scaling Shiny applications in production to hundreds or even thousands of users.

Defining custom neural network architectures isn't necessary. Learn how to build a 99% accurate image classifier with Transfer Learning and PyTorch.

How does R Shiny compare to drag and drop visualization tools like Tableau? Let's compare performance, aesthetics, and ease of use.

In this video, Appsilon Engineer Pedro Silva explains the basics of Shiny UI and provides multiple Shiny dashboard examples.

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.