文件名称:CNNcharacterrecog
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This project provides matlab class for implementation of convolutional neural networks. This networks was created by Yann LeCun and have sucessfully used in many practical applications, such as handwritten digits recognition, face detection, robot navigation and others
This release includes sample of handwritten digits recognition using CNN.
This release includes sample of handwritten digits recognition using CNN.
相关搜索： CNN
The
handwritten
digits
convolutional
neural
networks
face
recognition
using
neural
networks
Convolutional
robot
face
detection
neural
networks
Neural
robot
convolutional
neural
The
handwritten
digits
convolutional
neural
networks
face
recognition
using
neural
networks
Convolutional
robot
face
detection
neural
networks
Neural
robot
convolutional
neural
(系统自动生成,下载前可以参看下载内容)
下载文件列表
CNN for character recog
.......................\@cnn
.......................\....\adapt_dw.m
.......................\....\calchx.m
.......................\....\calcje.m
.......................\....\check_finit_dif.m
.......................\....\cnn.m
.......................\....\cnn_size.m
.......................\....\init.m
.......................\....\sim.m
.......................\....\subsasgn.m
.......................\....\subsref.m
.......................\....\train.m
.......................\back_conv2.m
.......................\back_subsample.m
.......................\cnet.mat
.......................\cnet_tool.m
.......................\eraser.gif
.......................\fastFilter2.m
.......................\preproc_data.m
.......................\preproc_image.m
.......................\rand_std.m
.......................\readMNIST.m
.......................\readMNIST_image.m
.......................\rot180.m
.......................\subsample.m
.......................\tansig_mod.m
.......................\test_dgt.m
.......................\train_cnn.m
.......................\@cnn
.......................\....\adapt_dw.m
.......................\....\calchx.m
.......................\....\calcje.m
.......................\....\check_finit_dif.m
.......................\....\cnn.m
.......................\....\cnn_size.m
.......................\....\init.m
.......................\....\sim.m
.......................\....\subsasgn.m
.......................\....\subsref.m
.......................\....\train.m
.......................\back_conv2.m
.......................\back_subsample.m
.......................\cnet.mat
.......................\cnet_tool.m
.......................\eraser.gif
.......................\fastFilter2.m
.......................\preproc_data.m
.......................\preproc_image.m
.......................\rand_std.m
.......................\readMNIST.m
.......................\readMNIST_image.m
.......................\rot180.m
.......................\subsample.m
.......................\tansig_mod.m
.......................\test_dgt.m
.......................\train_cnn.m