Blog
Welcome to the viso blog — our central destination for the latest updates, stories and industry news about visual deep learning and computer vision.
What Is Edge Computing? A Practical Overview
Edge Computing moves computing from the cloud to the network edge. On-device real-time data processing enables new applications.
An Introduction to Federated Learning: Challenges and Applications
Federated learning is used for distributed training of machine learning algorithms on multiple edge devices without exchanging training data.
Deep Learning for Person Re-Identification
Person re-identification uses Deep Learning to retrieve a person of interest across multiple cameras. An easy-to-read overview.
Edge AI – Driving Next-Gen AI Applications
Edge AI is the combination of edge computing and AI. Read why we need to push AI to the network edge and the advantages of Edge AI.
Deep Neural Network: The 3 Popular Types (MLP, CNN and RNN)
An easy-to-understand overview of a Deep Neural Network. Three types of Deep Neural Networks that are popular today.
Edge Intelligence in 2021 – Deep Learning And Edge Computing
Edge Intelligence or Edge AI moves AI computing from the cloud to edge devices, where data is generated. This is a key to AI democratization.
About the viso blog
Computer Vision Expertise
The viso blog provides a wide range of visual examples, animations and videos to showcase the power of visual AI. We cover real-world use cases of Computer Vision and surprising case studies about businesses using AI vision.
The Latest Industry news
Our goal is to make it easier for professionals, beginners and AI enthusiasts to find resources around AI vision and visual deep learning technology. We cover state-of-the-art computer vision software, algorithms and hardware.
Company and Product
The viso blog is powered by viso.ai – the company behind the Computer Vision platform Viso Suite. The blog is designed to grow and change as we do, so as we introduce new products and technologies you can follow us and find all the latest news here.