We'll show you how to import the proper libraries, set up a dashboard, add UI components, and more!

Have you considered adding AI/ML to your organization's operations? AI models can significantly reduce costs when they are implemented properly, but can end up in "purgatory" with the wrong approach. Here's how to properly implement AI models.

Learn how to find and use satellite image datasets for data science and machine learning projects with this beginner-friendly guide.

Can you build Shiny apps without any R knowledge? This R Shiny Dashboard Tutorial shows you my experience with learning R creating R Shiny dashboards in 2 days.

As part of our D4G Initiative and with the support of a Google grant, Appsilon Data Science will be contributing to to the work of biodiversity conservationists at the National Parks Agency in Gabon in collaboration with the University of Stirling.

Artificial Intelligence, Machine Learning, and Deep Learning models have demonstrated significant power to grow and improve businesses. This article explains how to properly approach developing an AI model.

Appsilon’s open source shiny.info package just received a major update and is now available on CRAN. A great solution for development and testing, shiny.info allows for displaying diagnostic information from inside Shiny apps.

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.

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.

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

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.

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.

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.

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.

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. 

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.

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.

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.

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.

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."

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.

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.

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.

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

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.

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.

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.

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

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.

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

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

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

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

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.