Here are the winners of our shiny.semantic PoContest as chosen by the Shiny team at R Studio. There's still time to vote for the People's Choice Award.

Appsilon team members Jędrzej Świeżewski, PhD and Damian Budelewski will be giving presentations at the Why R? Conference on November 12th.

Appsilon's list of tips for managing a successful kickoff meeting.

Vote for the People's Choice Award winner of the 2020 Appsilon shiny.semantic PoContest.

How do satellites take pictures? Can you process satellite images with R? Also, can you go to Space? Your questions meet our answers.

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

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.

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.

In this talk you’ll learn tools and best practices for making clean, reproducible R code in a working environment ready to be shared and productionized. We cover the benefits of version control, plumber, RStudio Connect, and many other tools.

In this presentation we present how to make use of the latest technical advancements in computer vision with an R setup even if they are originally implemented in Python.

Appsilon engineers Krystian Igras, Marcin Dubel, and Jędrzej Świeżewski, PhD will be giving virtual presentations on Friday, June 19th. Learn about xspliner, making production-ready R code, and using R for Machine Learning projects. 

Appsilon created a dashboard in collaboration the International Institute for Applied Systems Analysis to deliver information about COVID-19 to policymakers. We implemented the solution with Python and Svelte.

R Shiny has historically been used for data-driven applications and as a rapid development tool, but it's a highly adaptable technology. Is it possible to build a functioning video game in R Shiny? Let's find out.

Our favorite RStudio shortcuts, tips, and tricks to increase productivity while working with the RStudio IDE. We cover keyboard shortcuts, custom code snippets, addins, and more.

Dynamic Shiny Modules can destabilize the Shiny environment and cause its reactive graph to be rendered multiple times. Here is how to safely remove deleted module leftovers and make sure that your Shiny graph observers are rendered properly.

Distributed data science teams bring unique challenges. Managers may be looking for new tools. In this article we’ll explain how RStudio Connect helps organizations to properly organize teams and overcome the typical inefficiencies of remote work.

Introduction to object detection and image classification featuring the YOLO algorithm and its Darknet implementation

In this article, we focus on the technical aspects of the machine learning solution we implemented for the xView2 competition. We used PyTorch to build our models for satellite image analysis and fast.ai to develop their critical parts.

Our submission to the Pandemic Response Hackathon (CoronaRank) is inspired by Google’s PageRank and utilizes geolocation data in the Apache Parquet format from Veraset for effective exposure risk assessment using R and Markov Chain modeling.

This is the first of two blog posts about our recent participation in the Pandemic Response Hackathon. Our project (CoronaRank) was one of only 5 projects out of 230 submissions chosen to present at the closing ceremony.