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ver_0.82
- 此文件包包含用卷积神经网络识别手写输入的识别程序，数据可从MNIST下载-This release includes sample of handwritten digits recognition using CNN. If you just want to try it run cnet_tool. You ll see a simple GUI. It loads pretrained convolutional neural net from cnet.mat and recognize
CNNWB_05-27-2012
- 这个是我找到的卷积神经网络在minist库上数字识别最好的结果，准确率99.5 ，比C++版本的更好。程序可以直接编译运行，但是因为要下载两个数据库可能非常慢，需要你修改一下代码跳过去。如果你对cnn很感兴趣，可以找我Q:3617 28654-This is what I found convolutional neural network minist library Digital Identification best results, the accuracy was 99.5 , be
CNNS
- 这个程序旨在把卷积神经网络算法应用于手写字符识别。程序有几种结构的神经网络可以通过比较不同结构而得到对识别率的影响。-This program is designed to put the convolutional neural network algorithm is applied to the handwritten character recognition. The structure of the program there are several kinds of neural
source-code
- 用D-CNN的方法，即CNN卷积层加Fisher Vector做图像分类的MATLAB代码-Use D-CNN approach ( Fisher Vector follows results of CNN convolutional layer as the image feature) to classificate the image
mycnn
- 卷积神经网络识别字符的Matlab程序，包含所需的所有素材和自己改进的一部分代码-Convolutional neural network for handwriten digits recognition: training and simulation. This program implements the convolutional neural network for MNIST handwriten digits recognition, created by Yan
CNN-
- CNN卷积神经网络数字识别MATLAB代码-CNN convolutional neural network digital identification MATLAB code
cnn_tutorial
- 关于卷积神经网络的文章，中文翻译的，挺不错的，欢迎下载学习啊-A convolutional neural network tutorial, Chinese translation, very good, welcome to download the study
panwgcx688
- 卷积码是一种有记忆的编码,在任意给定的时间单元处,而且也与前m个输入有关,(Coding, convolutional code is a memory at any given time unit, but also related to m input before,)
CNN
- 这是卷积神经网络的小程序，希望对大家有帮助(This is a small convolutional neural network procedures, we hope to help)
FCIS-master
- 语义分割FCIS算法实现，可以在我的github上找到这个详细用法(Fully Convolutional Instance-aware Semantic Segmentation)
chinese_test
- 手写汉字识别，数据集训练，MNIST，Deep Convolutional Network识别手写汉字(Handwritten Chinese character recognition, data set training, MNIST, Deep Convolutional Network)
tf-pose-estimation-master
- OpenPose人体姿态识别项目是美国卡耐基梅隆大学（CMU）基于卷积神经网络和监督学习并以caffe为框架开发的开源库。可以实现人体动作、面部表情、手指运动等姿态估计。适用于单人和多人，具有极好的鲁棒性。是世界上首个基于深度学习的实时多人二维姿态估计应用，基于它的实例如雨后春笋般涌现。人体姿态估计技术在体育健身、动作采集、3D试衣、舆情监测等领域具有广阔的应用前景，人们更加熟悉的应用就是抖音尬舞机(OpenPost Human Attitude Recognition Project is a
1DCNN
- 使用卷积神经网络实现心电异常分类，内含损失函数，验证集、测试集等(ECG anomaly classification using convolutional neural network)