Miscellaneous and lightweight network tools

Overview

Network Tools

Collection of miscellaneous and lightweight network tools to simplify daily operations, administration, and troubleshooting of networks. Individual directories in this repository contain more detailed README.md files to better explain the scripts contained within.

Contact information:
Email: [email protected]
Twitter: @nickrusso42518

Owner
Nicholas Russo
CCDE #20160041, CCIE #42518 in Route/Switch and Service Provider, BS degree in CompSci from Rochester Institute of Technology (RIT). Father, husband, and USMC.
Nicholas Russo
A Pythonic library for Nvidia Codec.

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Zesen Qian 12 Dec 27, 2022
.NET bindings for the Pytorch engine

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ACV is a python library that provides explanations for any machine learning model or data.

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PyTorch implementation of DeepUME: Learning the Universal Manifold Embedding for Robust Point Cloud Registration (BMVC 2021)

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Yet Another Reinforcement Learning Tutorial

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Sungjoon 65 Dec 10, 2022
Real life contra a deep learning project built using mediapipe and openc

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An interactive DNN Model deployed on web that predicts the chance of heart failure for a patient with an accuracy of 98%

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The official homepage of the COCO-Stuff dataset.

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LIAO Shuiying 6 Dec 01, 2022
这是一个mobilenet-yolov4-lite的库,把yolov4主干网络修改成了mobilenet,修改了Panet的卷积组成,使参数量大幅度缩小。

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Codes for realizing theories learned from Data Mining, Machine Learning, Deep Learning without using the present Python packages.

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A PyTorch implementation of SIN: Superpixel Interpolation Network

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6 Sep 28, 2022
Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations

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[ICCV'21] Official implementation for the paper Social NCE: Contrastive Learning of Socially-aware Motion Representations

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VITA lab at EPFL 125 Dec 23, 2022
This package contains deep learning models and related scripts for RoseTTAFold

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Source code for the paper "PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction" in ACL2021

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197 Nov 26, 2022
Beyond imagenet attack (accepted by ICLR 2022) towards crafting adversarial examples for black-box domains.

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MXNet implementation for: Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution

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