Overview of deep learning architectures computers use to detect objects (with Video)

Deep neural networks have gained fame for their capability to process visual information. And in the past few years, they have become a key component of many computer vision applications. Among the key problems neural networks can solve is detecting and localizing objects in images. Object detection is used in many different domains, including autonomous driving, video surveillance, and healthcare. In this post, I will briefly review the deep learning architectures that help computers detect objects.

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NVIDIA sets another AI inference record in MLPerf

NVIDIA has set yet another record for AI inference in MLPerf with its A100 Tensor Core GPUs. MLPerf consists of five inference benchmarks which cover the main three AI applications today: image classification, object detection, and translation.

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How do Deep Neural Networks work?

Every day we are facing AI and neural network in some ways: from common phone use through face detection, speech or image recognition to more sophisticated — self-driving cars, gene-disease predictions, etc. We think it is time to finally sort out what AI consists of, what neural network is and how it works.

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Companies valuation neural network

Cityscape

Companies valuation is a cornerstone in areas such as corporate finance and investment decision-making. Since I am interested in both of these areas, from a professional point of view, one of the first projects that I decided to create and develop on the Keras library is a neural network for evaluating the company’s value. With this article, I would like to start talking about the…

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