This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. XGBoost is a widely used machine learning library, which uses gradient boosting techniques to incrementally build a better model during the training phase by combining multiple weak models. Weak models are generated by computing the gradient descent using […]
Learning to Rank with XGBoost and GPU Read More +
This article was originally published at MathWorks’ website. It is reprinted here with the permission of MathWorks. 3 Things You Need to Know A neural network is a computing model whose layered structure resembles the networked structure of neurons in the brain, with layers of connected nodes. A neural network can learn from data—so it
What Is a Neural Network? Read More +
This article was originally published at Tryolabs’ website. It is reprinted here with the permission of Tryolabs. Introduction Artificial intelligence (AI) and machine learning (ML) are among the top technology trends in the retail world. They are having a great impact on the industry, in particular in e-commerce companies that rely on online sales, where
The Guide to Machine Learning in Retail: Applications and Use Cases Read More +
This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. This post is the second in a series (Part 1) that addresses the challenges of training an accurate deep learning model using a large public dataset and deploying the model on the edge for real-time inference using NVIDIA
Building a Real-time Redaction App Using NVIDIA DeepStream, Part 2: Deployment Read More +
This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Some of the biggest challenges in deploying an AI-based application are the accuracy of the model and being able to extract insights in real time. There’s a trade-off between accuracy and inference throughput. Making the model more accurate
Building a Real-time Redaction App Using NVIDIA DeepStream, Part 1: Training Read More +
This article was originally published at Tryolabs’ website. It is reprinted here with the permission of Tryolabs. Introduction The fantasy that a machine is capable of simulating the human visual system is old. We’ve come a long way since the first university papers appeared back in the 1960s, as evidenced by the advent of modern
An Introductory Guide to Computer Vision Read More +
The PDF file linked to below is the white paper “Tools and Processors for Computer Vision: Selected Results from the Edge AI and Vision Alliance’s January 2020 Computer Vision Developer Survey”. For more information, please contact Jeff Bier at [email protected]. Edge AI and Vision Alliance January 2020 Computer Vision Developer Survey White Paper
Edge AI and Vision Alliance January 2020 Computer Vision Developer Survey (White Paper) Read More +
This article was originally published at Tryolabs’ website. It is reprinted here with the permission of Tryolabs. Introduction In the past few years, video analytics, also known as video content analysis or intelligent video analytics, has attracted increasing interest from both industry and the academic world. Thanks to the enormous advances made in deep learning,
A Guide to Video Analytics: Applications and Opportunities Read More +
This article was originally published at Tryolabs’ website. It is reprinted here with the permission of Tryolabs. Why edge computing? Humans are generating and collecting more data than ever. We have devices in our pockets that facilitate the creation of huge amounts of data, such as photos, gps coordinates, audio, and all kinds of personal
Machine Learning On Edge Devices: Benchmark Report Read More +
Should deep learning-based computer vision processing take place in the cloud, at the edge, or both? This seemingly simple question has a complicated answer: "it depends." This article provides perspectives on the various factors you should consider, and with what priorities, when making this implementation decision for your particular project's requirements. THe Edge-Centric Inference Evolution
Cloud and Edge Vision Processing Options for Deep Learning Inference Read More +
Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.
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