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DeepLearnToolbox-master

于 2020-12-16 发布
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下载积分: 1 下载次数: 3
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代码说明：

说明：  深度学习的工具箱，里面有卷积神经网络等的一些代码(Deep learning toolbox, which contains some codes such as convolutional neural network.)

文件列表：

DeepLearnToolbox-master\.travis.yml, 249 , 2015-12-01
DeepLearnToolbox-master\CAE\caeapplygrads.m, 1219 , 2015-12-01
DeepLearnToolbox-master\CAE\caebbp.m, 917 , 2015-12-01
DeepLearnToolbox-master\CAE\caebp.m, 1011 , 2015-12-01
DeepLearnToolbox-master\CAE\caedown.m, 259 , 2015-12-01
DeepLearnToolbox-master\CAE\caeexamples.m, 754 , 2015-12-01
DeepLearnToolbox-master\CAE\caenumgradcheck.m, 3618 , 2015-12-01
DeepLearnToolbox-master\CAE\caesdlm.m, 845 , 2015-12-01
DeepLearnToolbox-master\CAE\caetrain.m, 1148 , 2015-12-01
DeepLearnToolbox-master\CAE\caeup.m, 489 , 2015-12-01
DeepLearnToolbox-master\CAE\max3d.m, 173 , 2015-12-01
DeepLearnToolbox-master\CAE\scaesetup.m, 1937 , 2015-12-01
DeepLearnToolbox-master\CAE\scaetrain.m, 270 , 2015-12-01
DeepLearnToolbox-master\CNN\cnnapplygrads.m, 575 , 2015-12-01
DeepLearnToolbox-master\CNN\cnnbp.m, 2141 , 2015-12-01
DeepLearnToolbox-master\CNN\cnnff.m, 1774 , 2015-12-01
DeepLearnToolbox-master\CNN\cnnnumgradcheck.m, 3430 , 2015-12-01
DeepLearnToolbox-master\CNN\cnnsetup.m, 2020 , 2015-12-01
DeepLearnToolbox-master\CNN\cnntest.m, 193 , 2015-12-01
DeepLearnToolbox-master\CNN\cnntrain.m, 845 , 2015-12-01
DeepLearnToolbox-master\CONTRIBUTING.md, 544 , 2015-12-01
DeepLearnToolbox-master\create_readme.sh, 744 , 2015-12-01
DeepLearnToolbox-master\data\mnist_uint8.mat, 14735220 , 2015-12-01
DeepLearnToolbox-master\DBN\dbnsetup.m, 557 , 2015-12-01
DeepLearnToolbox-master\DBN\dbntrain.m, 232 , 2015-12-01
DeepLearnToolbox-master\DBN\dbnunfoldtonn.m, 425 , 2015-12-01
DeepLearnToolbox-master\DBN\rbmdown.m, 90 , 2015-12-01
DeepLearnToolbox-master\DBN\rbmtrain.m, 1401 , 2015-12-01
DeepLearnToolbox-master\DBN\rbmup.m, 89 , 2015-12-01
DeepLearnToolbox-master\LICENSE, 1313 , 2015-12-01
DeepLearnToolbox-master\NN\nnapplygrads.m, 628 , 2015-12-01
DeepLearnToolbox-master\NN\nnbp.m, 1638 , 2015-12-01
DeepLearnToolbox-master\NN\nnchecknumgrad.m, 704 , 2015-12-01
DeepLearnToolbox-master\NN\nneval.m, 811 , 2015-12-01
DeepLearnToolbox-master\NN\nnff.m, 1849 , 2015-12-01
DeepLearnToolbox-master\NN\nnpredict.m, 192 , 2015-12-01
DeepLearnToolbox-master\NN\nnsetup.m, 1844 , 2015-12-01
DeepLearnToolbox-master\NN\nntest.m, 184 , 2015-12-01
DeepLearnToolbox-master\NN\nntrain.m, 2414 , 2015-12-01
DeepLearnToolbox-master\NN\nnupdatefigures.m, 1858 , 2015-12-01
DeepLearnToolbox-master\README.md, 8861 , 2015-12-01
DeepLearnToolbox-master\README_header.md, 2244 , 2015-12-01
DeepLearnToolbox-master\REFS.md, 950 , 2015-12-01
DeepLearnToolbox-master\SAE\saesetup.m, 132 , 2015-12-01
DeepLearnToolbox-master\SAE\saetrain.m, 308 , 2015-12-01
DeepLearnToolbox-master\tests\runalltests.m, 165 , 2015-12-01
DeepLearnToolbox-master\tests\test_cnn_gradients_are_numerically_correct.m, 552 , 2015-12-01
DeepLearnToolbox-master\tests\test_example_CNN.m, 981 , 2015-12-01
DeepLearnToolbox-master\tests\test_example_DBN.m, 1031 , 2015-12-01
DeepLearnToolbox-master\tests\test_example_NN.m, 3247 , 2015-12-01
DeepLearnToolbox-master\tests\test_example_SAE.m, 934 , 2015-12-01
DeepLearnToolbox-master\tests\test_nn_gradients_are_numerically_correct.m, 749 , 2015-12-01
DeepLearnToolbox-master\util\allcomb.m, 2618 , 2015-12-01
DeepLearnToolbox-master\util\expand.m, 1958 , 2015-12-01
DeepLearnToolbox-master\util\flicker.m, 208 , 2015-12-01
DeepLearnToolbox-master\util\flipall.m, 80 , 2015-12-01
DeepLearnToolbox-master\util\fliplrf.m, 543 , 2015-12-01
DeepLearnToolbox-master\util\flipudf.m, 576 , 2015-12-01
DeepLearnToolbox-master\util\im2patches.m, 313 , 2015-12-01
DeepLearnToolbox-master\util\isOctave.m, 108 , 2015-12-01
DeepLearnToolbox-master\util\makeLMfilters.m, 1895 , 2015-12-01
DeepLearnToolbox-master\util\myOctaveVersion.m, 169 , 2015-12-01
DeepLearnToolbox-master\util\normalize.m, 97 , 2015-12-01
DeepLearnToolbox-master\util\patches2im.m, 242 , 2015-12-01
DeepLearnToolbox-master\util\randcorr.m, 283 , 2015-12-01
DeepLearnToolbox-master\util\randp.m, 2083 , 2015-12-01
DeepLearnToolbox-master\util\rnd.m, 49 , 2015-12-01
DeepLearnToolbox-master\util\sigm.m, 48 , 2015-12-01
DeepLearnToolbox-master\util\sigmrnd.m, 126 , 2015-12-01
DeepLearnToolbox-master\util\softmax.m, 256 , 2015-12-01
DeepLearnToolbox-master\util\tanh_opt.m, 54 , 2015-12-01
DeepLearnToolbox-master\util\visualize.m, 1072 , 2015-12-01
DeepLearnToolbox-master\util\whiten.m, 183 , 2015-12-01
DeepLearnToolbox-master\util\zscore.m, 137 , 2015-12-01
DeepLearnToolbox-master\CAE, 0 , 2015-12-01
DeepLearnToolbox-master\CNN, 0 , 2015-12-01
DeepLearnToolbox-master\data, 0 , 2015-12-01
DeepLearnToolbox-master\DBN, 0 , 2015-12-01
DeepLearnToolbox-master\NN, 0 , 2015-12-01
DeepLearnToolbox-master\SAE, 0 , 2015-12-01
DeepLearnToolbox-master\tests, 0 , 2015-12-01
DeepLearnToolbox-master\util, 0 , 2015-12-01
DeepLearnToolbox-master, 0 , 2015-12-01

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发表评论

0 个回复

  • Hallen3
    用MOM求解HALLEN S INTEGRAL EQUATION ，得出电流分布(Using MOM to solve HALLEN' S INTEGRAL EQUATION, draw current distribution)
    2009-04-06 22:01:08下载
    积分：1
  • matlab
    一些关于遗传算法的例子，里面是精简的各种特色的遗传算法例子(Some examples of about genetic algorithm, the inside is concise characteristic of various kinds of genetic algorithm example)
    2012-03-23 16:27:46下载
    积分：1
  • exercises
    a good book on numerical pde and its miles !
    2011-09-27 21:32:37下载
    积分：1
  • L_D
    用Matlab程序实现P阶Levinson-Durbin算法。以一个2阶自回归模型（参数为b0=1, a1=0, a2=0.81）和一个2阶滑动平均模型（参数为b0=1, b1=1, b2=1）为例，选取观测数据长度为1000，分别用一个AR(2)模型和一个AR(10)阶模型来估计其功率谱。设激励信号模型的高斯白噪声的均值为0，方差为1。用Levinson-Durbin算法迭代计算AR模型参数，并用估计出的AR模型参数画出观测信号的功率谱。并对Levinson-Durbin算法的性能进行分析。(Write a small MATLAB program that implements the pthorder Levinson-Durbin (L-D). Run/Test the program using a AR(2) process (b0=1,a1=0, a2=0.81) and an MA(2) (bn=1,1,1) process-about 1000 samples. Use L-D with p=2 (for the AR) and 10 (for the MA). Plot the AR spectra produced in the two cases with L-D. List the direct form and the reflection coefficients in a table. Profile the L-D (total number of computations for a pthorder)
    2009-12-29 01:39:11下载
    积分：1
  • localization_m70
    it is based on matlab simulator
    2013-04-06 17:13:20下载
    积分：1
  • mpeg_MLWDF
    资源调度算法。MPEG业务在MLWEP准则下的资源调度性能。(Resource allocation algorithm. MPEG business MLWEP resource scheduling performance criteria.)
    2010-07-28 12:20:55下载
    积分：1
  • rayandawgnbpsk
    分别在瑞利信道和白噪声信道下bpsk误码性能对比(ray awgn bpsk NSR)
    2010-11-02 19:59:30下载
    积分：1
  • fdtddangbanxishou
    基于FDTD的含挡板的平行板MATLAB电磁仿真(Content-based FDTD parallel baffle plate electromagnetic simulation of MATLAB)
    2009-07-12 10:41:02下载
    积分：1
  • code
    Hough Transform for Iris
    2014-01-01 08:53:07下载
    积分：1
  • Kmeans_test
    说明：  K-Means聚类算法，内附参考文献，亲测可用，性能良好，效果极为明显，完全适用于各种数据维度的数据集分类，欢迎下载使用。(K-Means clustering algorithm, pro-test available.)
    2019-03-29 20:47:26下载
    积分：1
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