搜索资源列表
Ver_0.72
- CNN Class, ver 0.72. Change log: Ver 0.72: Sample GUI added, demonstrating use of convolutional network for handwriten digits recognition. Training runs 20 faster. Ver 0.71: Bug fix: training was stoped after 1 epoch. Ver 0.70:
charrec
- char recognition with c-char recognition with cnn
CNN
- This project provides matlab class for implementation of convolutional neural networks. This networks was developed by Yann LeCun and have sucessfully used in many practical applications, such as handwritten digits recognition, face detection, robot
cnn
- 用卷积神经网络实现字母图像的识别，里面包含数据集，可直接使用-Achieve recognition letters image convolution neural network, which includes data collection, can be used directly
CNN
- 用 卷积神经网络进行手写字符 识别,内含mnist训练集-Handwritten character recognition, containing mnist convolution neural network training set
mc-cnn
- 通过深度神经网络实现双目视觉下图像深度识别，需要运行在linux环境中-Achieve recognition image depth through binocular visual depth at the neural network, you need to run in the linux environment
CNN
- CNN learning 手写数字识别 神经网络-CNN learning handwritten numeral recognition neural network
tensorflow-cnn
- 基于TensorFlow的mnist数据集识别，使用CNN的方法，采用梯度下降学习(MNIST data set recognition based on TensorFlow, using CNN method, using gradient descent learning)
CNN Matlab代码
- 利用大量图像数据对卷积神经网络算法进行训练，通过卷积、池化、下采样以及全连接层训练后的卷积神经网络在图像识别精度越来越高。(By using a large number of image data to train the convolutional neural network algorithm, the accuracy of the image recognition is higher and higher by convolution, pooling, down sampling
CNN-123
- 区别于传统的图像识别，将一维的数据放入cnn网络进行识别(Different from the traditional image recognition, one-dimensional data into the cnn network to identify)
Desktop
- 利用tensorflow建立以个用于面部表情识别的模型(Using tensorflow to build a model for facial expression recognition)
CNN_OCR
- 平台MXNet上实现基于CNN的光学字符数字识别。(Implementation of optical character recognition based on CNN on platform MXNet.)
cnn-示例
- 卷积神经网络的结构模型，可实现对图像进行训练与识别(The structure model of the convolution neural network can realize the training and recognition of the image)
CNN
- 卷积神经网络是近年发展起来，并引起广泛重视的一种高效识别方法。20世纪60年代，Hubel和Wiesel在研究猫脑皮层中用于局部敏感和方向选择的神经元时发现其独特的网络结构可以有效地降低反馈神经网络的复杂性，继而提出了卷积神经网络（Convolutional Neural Networks-简称CNN）。现在，CNN已经成为众多科学领域的研究热点之一，特别是在模式分类领域，由于该网络避免了对图像的复杂前期预处理，可以直接输入原始图像，因而得到了更为广泛的应用。(Convolution neura
CNN
- 手写体识别的训练，采用卷积神经网络，附带数据集下载代码(The training of handwritten recognition is based on convolution neural network, and the download from the dataset.)
张哲_017034910051_03
- 基于tensorflow的手写数字识别，MLP和CNN对比(the compare between MLP and CNN in Handwriting recognition.)
utf8''Traffic-sign-recognition
- 项目基于Tensorflow进行实现。 #### 文件说明： --- * input_data.py: 图片的输入 * traffic_sign_cnn.py: 用cnn进行训练分类 * testDemo.py: 用于测试已经训练出来的模型，输入单个图片输出结果，并分类到文件夹 #### 数据集说明： --- * 这里是列表文本使用的是比利时的交通标志数据集，可以网上自己找，里面有62个分类。 #### 网络说明： --- *
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
- 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-figure-recognize
- 本程序主要包括：人脸识别、图像采集、模型训练等功能(Face recognition, image acquisition, model training)