meetsiyuan Goto Github PK

1d-cnn-for-cwru

1D CNN for CWRU rolling bearings dataset

awesome-pruning

A curated list of neural network pruning resources.

bearing-fault-prediction

轴承有3种故障：外圈故障，内圈故障，滚珠故障，外加正常的工作状态。如表1所示，结合轴承的3种直径（直径1,直径2,直径3），轴承的工作状态有10类

channel_prune

cifar10dataset

Create your own dataset with the similar format with CIFAR10 in python version.

compressed-sensing-code

自己搜集和改写的压缩感知相关程序代码

cwru

cwru-1

Multiclass classification of vibration signals of faulty bearings

cwru-attentionmechanism-resnet

This is the source code in the paper "Temporal attention mechanism for bearing fault diagnosis in nuclear power plant rotating machines"

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

download

Lantern官方版本下载 蓝灯 翻墙 代理 科学上网 外网 加速器 梯子 路由 proxy vpn circumvention gfw

fault-diagnosis

Fault Diagnosis on Bearing Dataset

filter-pruning-geometric-median

Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (CVPR 2019 Oral)

jd20210916

CEEMDAN+SampleEntropy+LSTM+RF

lrp_pruning

makedown

mcts

本科毕设

mechanical-fault-diagnosis-based-on-deep-learning

CNN for mechanical fault diagnosis

micronet

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

neve

A fast, lightweight, AMP ready WordPress theme built with speed and usability in mind.

nlp-nonlinear-regression-using-1d-cnn

This repository include a one dimensional Convolutional Neural network. The used dataset is California Housing dataset.The Solution is nonlinear regression model

notes-system

The notebook system with its regulations and templates.

paper1_changechanneldone

pruning_filters_for_efficient_convnets

PyTorch implementation of "Pruning Filters For Efficient ConvNets"

pytorch-pruning

PyTorch Implementation of [1611.06440] Pruning Convolutional Neural Networks for Resource Efficient Inference

pytorch_resnet_cifar10

Proper implementation of ResNet-s for CIFAR10/100 in pytorch that matches description of the original paper.

pytorch_squeezenet

Implementation of Squeezenet in pytorch, pretrained models on Cifar 10 data to come

soft-filter-pruning

Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

supergraph

svd-evolutive-cnn

Toy example of a tool to optimize CNN layers widths, according their SVD decomposition