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Getting Started With Image Classification: fastai, ResNet, MobileNet, and More
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.
![PP-YOLO Object Detection Algorithm: Why It's Faster than YOLOv4 [2021 UPDATED]](https://cdn.prod.website-files.com/654fd3ad88635290d9845b9e/65b39f55da88975b21ae6f33_6525256482c9e9a06c7a9d3c%252F65aab94b6509044d2c434fc8_appsilon_gathering_hero.webp)
PP-YOLO Object Detection Algorithm: Why It's Faster than YOLOv4 [2021 UPDATED]
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.

Remote Data Science Team Best Practices: Scrum, GitHub, Docker, and More
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.

xspliner: An R Package to Build Explainable Surrogate ML Models
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.

eRum 2020: Appsilon Presentations On xspliner, fast.ai, and Writing Production-Ready R Code
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.
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