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DeepLearnToolbox-master

于 2021-04-13 发布 文件大小:14397KB
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代码说明：

  深度学习工具包，包含神经网络，卷积神经网络等众多深度学习工具的MATLAB源码(MATLAB source deep learning kits, including neural networks, neural networks, and many other convolution depth learning tool)

文件列表：

DeepLearnToolbox-master
.......................\.travis.yml,249,2015-12-01
.......................\CAE
.......................\...\caeapplygrads.m,1219,2015-12-01
.......................\...\caebbp.m,917,2015-12-01
.......................\...\caebp.m,1011,2015-12-01
.......................\...\caedown.m,259,2015-12-01
.......................\...\caeexamples.m,754,2015-12-01
.......................\...\caenumgradcheck.m,3618,2015-12-01
.......................\...\caesdlm.m,845,2015-12-01
.......................\...\caetrain.m,1148,2015-12-01
.......................\...\caeup.m,489,2015-12-01
.......................\...\max3d.m,173,2015-12-01
.......................\...\scaesetup.m,1937,2015-12-01
.......................\...\scaetrain.m,270,2015-12-01
.......................\CNN
.......................\...\cnnapplygrads.m,575,2015-12-01
.......................\...\cnnbp.m,2141,2015-12-01
.......................\...\cnnff.m,1774,2015-12-01
.......................\...\cnnnumgradcheck.m,3430,2015-12-01
.......................\...\cnnsetup.m,2020,2015-12-01
.......................\...\cnntest.m,193,2015-12-01
.......................\...\cnntrain.m,845,2015-12-01
.......................\CONTRIBUTING.md,544,2015-12-01
.......................\create_readme.sh,744,2015-12-01
.......................\data
.......................\....\mnist_uint8.mat,14735220,2015-12-01
.......................\DBN
.......................\...\dbnsetup.m,557,2015-12-01
.......................\...\dbntrain.m,232,2015-12-01
.......................\...\dbnunfoldtonn.m,425,2015-12-01
.......................\...\rbmdown.m,90,2015-12-01
.......................\...\rbmtrain.m,1401,2015-12-01
.......................\...\rbmup.m,89,2015-12-01
.......................\htm" target=_blank>LICENSE,1313,2015-12-01
.......................\NN
.......................\..\nnapplygrads.m,628,2015-12-01
.......................\..\nnbp.m,1638,2015-12-01
.......................\..\nnchecknumgrad.m,704,2015-12-01
.......................\..\nneval.m,811,2015-12-01
.......................\..\nnff.m,1849,2015-12-01
.......................\..\nnpredict.m,192,2015-12-01
.......................\..\nnsetup.m,1844,2015-12-01
.......................\..\nntest.m,184,2015-12-01
.......................\..\nntrain.m,2414,2015-12-01
.......................\..\nnupdatefigures.m,1858,2015-12-01
.......................\README.md,8861,2015-12-01
.......................\README_header.md,2244,2015-12-01
.......................\REFS.md,950,2015-12-01
.......................\SAE
.......................\...\saesetup.m,132,2015-12-01
.......................\...\saetrain.m,308,2015-12-01
.......................\tests
.......................\.....\runalltests.m,165,2015-12-01
.......................\.....\test_cnn_gradients_are_numerically_correct.m,552,2015-12-01
.......................\.....\test_example_CNN.m,981,2015-12-01
.......................\.....\test_example_DBN.m,1031,2015-12-01
.......................\.....\test_example_NN.m,3247,2015-12-01
.......................\.....\test_example_SAE.m,934,2015-12-01
.......................\.....\test_nn_gradients_are_numerically_correct.m,749,2015-12-01
.......................\util
.......................\....\allcomb.m,2618,2015-12-01
.......................\....\expand.m,1958,2015-12-01
.......................\....\flicker.m,208,2015-12-01
.......................\....\flipall.m,80,2015-12-01
.......................\....\fliplrf.m,543,2015-12-01
.......................\....\flipudf.m,576,2015-12-01
.......................\....\im2patches.m,313,2015-12-01
.......................\....\isOctave.m,108,2015-12-01
.......................\....\makeLMfilters.m,1895,2015-12-01
.......................\....\myOctaveVersion.m,169,2015-12-01
.......................\....\normalize.m,97,2015-12-01
.......................\....\patches2im.m,242,2015-12-01
.......................\....\randcorr.m,283,2015-12-01
.......................\....\randp.m,2083,2015-12-01
.......................\....\rnd.m,49,2015-12-01
.......................\....\sigm.m,48,2015-12-01
.......................\....\sigmrnd.m,126,2015-12-01
.......................\....\softmax.m,256,2015-12-01
.......................\....\tanh_opt.m,54,2015-12-01
.......................\....\visualize.m,1072,2015-12-01
.......................\....\whiten.m,183,2015-12-01
.......................\....\zscore.m,137,2015-12-01

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    积分：1
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    2020-06-05 17:01:10下载
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    2015-02-14 17:06:40下载
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    2021-03-02 16:39:34下载
    积分：1
  • 2177e8d3adb15fabed33322ffd32afc8394a
    说明：  an investigation on use of chaotic sequence in DS-CDMA. The DS-CDMA systems offer physical layer security without the need for a significant increase in computation or power requirements. Nowadays, DSCDMA has been used widely. These systems suffers from multiple access interference because of other users transmitting in the cell, channel inter symbol interference and additive white Gaussian noise. Codes have an effective role in DSCDMA system, so M-sequences; gold sequences have been used as spreading codes in DS-CDMA. These sequences by shift registers and periodic in nature are developed. However, these sequences are not enough and also limit the security. This paper presents an investigation on use of new type of sequences called chaotic sequences for DS-CDMA system. These sequences by chaotic maps are generated.
    2019-03-13 22:41:27下载
    积分：1
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