搜索资源列表
CNN
- 我编的CNN c++程序，对CNN学习者可能有用-I addendum to the CNN c procedures, which may be useful for learners CNN
cnn.rar
- canny边缘检测源代码，边缘检测对于图像理解、目标检测、跟踪等问题非常重要，该代码实现了canny算子做边缘检测,source code canny edge detection, edge detection for image understanding, target detection, tracking and other issues is very important that the code to do the canny edge detection operator
cnn
- vs的cnn程序，没有调用任何库。有两个卷积层，用minst手写库识别(Vs's CNN program, no library is called. There are two coiling layers that are identified by a MINST handwritten Library)
cnn
- 手写数字识别的简单实现，CNN入门到深入，三个版本供读者使用。(Simple Implementation of Handwritten Number Recognition, Getting Started with CNN)
CNN
- 模型,出自深度学习的模型，只有配套函数，主函数需要自己写(model cnn Deeplearningtoolbox)
Two-Layer-CNN-on-MNIST-master
- cnn Mnist 两层卷积实现，可以达到97%d 识别率，大家可以拿来上手试用(CNN MNIST two layers)
卷积神经网络CNN代码解析-matlab
- 卷积神经网络CNN的代码解析文档，可以辅助了解CNN的MATLAB程序实现过程(CNN code analysis document of convolution neural network)
CNN code
- 处理一维振动信号，用于故障分类，模式识别(1D CNN for fault classification)
基于Tensorflow的CNN数字识别
- 本文实现了基于minist的手写数字识别，基于TensorFlow的python语言，程序有详细的注释，以及手把手教你怎么搭建CNN(This article implements the handwritten numeral recognition based on minist, the python language based on TensorFlow, the program has detailed annotations, and hands-on instructions o
CNN
- 完整的CNN卷积神经网络创建、训练及测试。(Complete CNN convolution neural network is created, trained and tested.)
cnn
- cnn 分类 一维 信号分类 模型简单适合新手，效果一般(Cnn classification One-dimensional signal classification The model is simple for beginners, the effect is average)
CNN
- 手写数字识别的数据集 matlab实现cnn(Data Set for Handwritten Number Recognition Realization of CNN in matlab)
CNN实现道路特征提取
- 采用CNN神经网络道路特征提取和故障诊断(Road feature extraction and Fault diagnosis based on CNN Neural Network)
cnn人脸识别
- 使用CNN实现人脸识别，包括训练数据集与测试数据集(Face recognition using CNN, including training data set and test data set)
cnn-edge-detection
- 卷积神经网络实现图像的边缘检测+python代码(CNN-edge-detection-python-codes)
CNN
- 卷积神经网络分类 调制信号识别 卷积神经网络（Convolutional Neural Networks, CNN）是一类包含卷积计算且具有深度结构的前馈神经网络（Feedforward Neural Networks），是深度学习（deep learning）的代表算法之一 [1-2] 。卷积神经网络具有表征学习（representation learning）能力，能够按其阶层结构对输入信息进行平移不变分类（shift-invariant classification），因此也被称
CNN
- THU微纳电子系ic设计课程大作业，使用verilog实现CNN加速器，含一层卷积和池化，仿真通过。(a CNN accelerator written in VerilogHDL, including one conv layer and one pooling layer, simulation passed)
CNN-SVM
- 卷积神经网络（CNN）用来提取特征，采用SVM分类器进行训练和分类(Convolutional Neural Network (CNN) was used to extract features, and SVM classifier was used for training and classification)
CNN+LSTM EGG项目
- 使用LSTM +CNN对EGG 进行分类预测，一维CNN提取数字信息特征，LSTM 进行分类预测(CNN+LSTM EGG sinals)
1D-CNN
- 1D-CNN的模型、训练与预测。用于时间序列的一种信号处理。(the model of 1D-CNN with trainning and prediction)