Convolutional Neural Networks Programming Tutorials & Engineering Articles

The article discusses the application of relational graph learning, specifically relational graph convolutional networks (RGCN), to detect collusion in fraudulent activities within the Uber platfor...

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.

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...

This article delves into the object detection inference process, specifically focusing on the YOLOv3 model.

The article discusses Image GPT, a generative model that applies the transformer architecture used in language models to image generation.

The article discusses how Uber Eats utilizes graph learning techniques to enhance its food recommendation system, improving user experience by providing personalized dish and restaurant suggestions.

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...

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...

The article discusses the role of women in data science at Uber, highlighting their contributions to various projects and the importance of diversity in technical fields.

The article discusses an innovative approach to enhance the performance of Convolutional Neural Networks (CNNs) by utilizing JPEG's internal representations.

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.

The article discusses the limitations of Convolutional Neural Networks (CNNs) in performing coordinate transformations and introduces the CoordConv layer as a solution.

The article discusses the importance of accurately segmenting building footprints using a novel framework called Deep Structured Active Contours (DSAC).

The article presents a novel approach for semi-automatic annotation of object instances in images, shifting from traditional pixel-labeling to polygon prediction.

The article discusses SBNet, an open-source algorithm developed by Uber ATG that leverages activation block sparsity to enhance the speed of Convolutional Neural Networks (CNNs).

The article discusses the Reversible Residual Network (RevNet), a variant of Residual Networks (ResNets) that allows for backpropagation without storing intermediate activations, thereby reducing m...

The article discusses the capabilities of NVIDIA DIGITS 5 for image segmentation, highlighting its integrated workflow and model store.

This article provides a comprehensive overview of deep learning, focusing on its historical development and training methodologies.

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...

The article discusses Pinnability, a machine learning initiative by Pinterest aimed at enhancing the personalization of the home feed by predicting which Pins users are likely to engage with.

The article discusses the NVIDIA cuDNN library, a GPU-accelerated library designed to optimize deep neural networks (DNNs) for machine learning applications.