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How NVIDIA Uses Convolutional Neural Networks

10 engineering articles about Convolutional Neural Networks from NVIDIA's engineering team

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NVIDIA
Intermediate
This article is the second part of a series on building deep learning-powered recommender systems, focusing on the application of deep learning techniques to enhance recommendation quality.
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NVIDIA
Intermediate
This article introduces cuSignal, a library within the RAPIDS ecosystem designed for signal processing using NVIDIA GPUs, which significantly accelerates computations compared to traditional method...
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Advanced
This article delves into the object detection inference process, specifically focusing on the YOLOv3 model.
Sanjay Dulepet
13 min read
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Advanced
The article discusses NVIDIA's contributions to the CVPR 2019 conference, highlighting 20 accepted papers and posters, including advancements in semantic image synthesis, video action detection, an...
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Advanced
The article discusses the NVIDIA Transfer Learning Toolkit, now known as the NVIDIA TAO Toolkit, and its model pruning feature, which enhances the efficiency of deep learning models by reducing the...
Greg Heinrich
15 min read
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NVIDIA Researchers are set to present 17 papers and posters at the European Conference on Computer Vision (ECCV) 2018, showcasing advancements in AI and computer vision.
Nefi Alarcon
16 min read
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Advanced
The article discusses the capabilities of NVIDIA DIGITS 5 for image segmentation, highlighting its integrated workflow and model store.
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Intermediate
This article provides a comprehensive overview of deep learning, focusing on its historical development and training methodologies.
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Intermediate
The article discusses the application of deep learning, specifically Convolutional Neural Networks (CNNs), in identifying geological processes on Mars, such as volcanic rootless cones and impact cr...
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The article discusses the NVIDIA cuDNN library, a GPU-accelerated library designed to optimize deep neural networks (DNNs) for machine learning applications.

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