搜索资源列表
Caffe-Python-Tutorial-master
- 深度学习下的卷积神经网络 剪枝算法 CNN(Deep learning Prune for CNN Deep learning Prune for CNN)
flower_CNN
- 简单的的CNN对花进行分类，里面包括代码和数据(Using CNN to classify the flower,which include data and code)
MyCode
- cnn结合xgboost代码,用于多分类器的设计(CNN related code,combined with xgboost,it is used to classify things.)
CNN
- 一种有效的特征提取算法，包含几类卷积神经网络算法代码与演示数据(An effective feature extraction algorithm includes several kinds of convolutional neural network algorithm codes and demonstration data.)
CNN
- 通过卷积网络，自动实现对图片特征的提取，通过训练，得到有效的权值，进行图像分类(Through convolution network, automatic extraction of image features can be realized. Through training, effective weights can be obtained and image classification can be carried out
1D-CNN
- 一维信号的深度学习算法和例子包括CNN、DBN等，有详细的说明(Deep Learning Algorithms and Examples for One-Dimensional Signals)
Deep learning_CNN DBN RBM
- 运用深度学习模型实现图像的分类，主要包括卷积神经网络CNN和深信度网络DBN(Classification of images using deep learning model includes convolutional neural network CNN and belief network DBN.)
CNN_P300-master
- cnn网络构建神经网络训练脑机接口P300模型(Construction of Brain-Computer Interface P300 Model for Neural Network Training Based on CNN Network)
classifier_cnn
- 利用MATLAB实现一个基于CNN的图像分类算法(Implementing an image classification algorithm based on CNN with MATLAB)
CNN电池1×9维诊断20181105
- 通过卷积神经网络进行对一维信号的故障诊断(Fault Diagnosis by CNN)
CNN电池1×9维诊断20181112
- 利用深度学习cnn的电池不一致性故障诊断(Battery fault diagnosis using deep learning CNN)
CNN-FPGA-master
- 用FPGA实现CNN算法，实现CNN加速(Realization of CNN Algorithms with FPGA)
跨模态文字检索图片102花卉数据
- 跨模态检索 tensorflow实现，使用googlenet处理图片，char-cnn处理文字，使用triple-loss训练(Tensorflow is implemented by cross-modal retrieval, using Google eNet to process pictures, char-cnn to process text, and triple-loss training)
MyCNNPSO
- 采用PSO对CNN进行优化，然后用于数据预测，一个初步的实验，精度有待提高(PSO is used to optimize CNN and then to predict data. A preliminary experiment shows that the accuracy needs to be improved.)
CNN
- 用深度卷积神经网络进行图像盲取证，定位复制粘贴篡改区域(Blind forensics of image using deep convolution neural network to locate copy-paste tampered area)
symbol_resnet
- RACNN注意力机制，细腻度图片分类。 RA-CNN由上到下用了3个尺度并且越来越精细，尺度间构成循环，即上层的输出作为当层的输入。RA-CNN主要包含两部分：每一个尺度上的卷积网络和相邻尺度间的注意力提取网络(APN, Attention Proposal Network)。在每一个尺度中，使用了堆叠的卷积层等，最后接上全连接层于softmax层，输出每一个类别的概率；这个是很好理解的，代码采用的网络结构是VGG的网络结构。(RACN
CNN
- 这是一个为1D心电图数据训练而设计的神经网络。(this is a Covoluntional Neural Network deisigned for 1D ECG data training.)
CNN_v2
- 癫痫脑电图（EEG）异常波精准识别深度学习CNN卷积神经网络(Accurate Recognition of Epilepsy EEG Abnormal Waves and Deep Learning CNN Convolutional Neural Network)
CNN
- 该代码文件全称为卷积神经网络，这是深度学习神经网络里面处理图片比较好的网络。(The code file is called convolutional neural network, which is a better network for processing pictures in deep learning neural network.)
CNN_matlab
- 使用matlab2019a深度学习工具箱实现的CNN卷积神经网络分类例程，数据是随机生成的一维随机数(Using the CNN convolution neural network classification routine realized by MATLAB 2019a deep learning toolbox, the data is one-dimensional random number generated rando