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DeepLearnToolbox-master

于 2021-03-21 发布
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说明：  该工具包提供了一个用于通过算法、预训练模型和应用程序来设计和实现深度神经网络的框架。您可以使用卷积神经网络（ConvNet、CNN）和长短期记忆 (LSTM) 网络对图像、时序和文本数据执行分类和回归。应用程序和绘图可帮助您可视化激活值、编辑网络架构和监控训练进度。(The toolbox provides a framework for designing and implementing deep neural networks through algorithms, pre training models and applications. You can use convolutional neural networks (convnet, CNN) and long and short term memory (LSTM) networks to perform classification and regression on image, temporal, and text data. Applications and graphics help you visualize activation values, edit network architecture, and monitor training progress.)

文件列表：

DeepLearnToolbox-master, 0 , 2021-03-06
DeepLearnToolbox-master\.travis.yml, 249 , 2015-12-01
DeepLearnToolbox-master\CAE, 0 , 2021-03-06
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, 0 , 2021-03-06
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\CNN\test_example_CNN.m, 981 , 2015-12-01
DeepLearnToolbox-master\CONTRIBUTING.md, 544 , 2015-12-01
DeepLearnToolbox-master\DBN, 0 , 2021-03-06
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, 0 , 2021-03-06
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, 0 , 2021-03-06
DeepLearnToolbox-master\SAE\saesetup.m, 132 , 2015-12-01
DeepLearnToolbox-master\SAE\saetrain.m, 308 , 2015-12-01
DeepLearnToolbox-master\create_readme.sh, 744 , 2015-12-01
DeepLearnToolbox-master\data, 0 , 2021-03-06
DeepLearnToolbox-master\data\mnist_uint8.mat, 14735220 , 2015-12-01
DeepLearnToolbox-master\tests, 0 , 2021-03-06
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, 0 , 2021-03-06
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

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发表评论

0 个回复

  • ofdm-qpsk-mycreatechannel
    ofdm系统设计，采用qpsk调制，可以轻松改变信道条件，比较研究，是非常方便的工具，我用了很多次了，很不错哦！！！(ofdm system using qpsk modulation, you can easily change the channel conditions, comparative study, it is very convenient tool, I use a lot of times, very good, oh! ! !)
    2010-05-06 22:16:34下载
    积分：1
  • pca
    matlab algorithm，PCA (Principal component analysis)used for dimensionality reduction. usage:[NEW_DATA, EIGVECTOR, EIGVALUE] = PCA(DATA, EIG_N)
    2011-08-19 21:38:56下载
    积分：1
  • newton
    数值分析 孙志忠 上机题 牛顿法,计算机(Numerical analysis of Newton' s method RAIL-on title)
    2014-11-22 19:31:46下载
    积分：1
  • fine
    说明：  ecg信号读取与二进制到十进制转换，为处理做准备(read ecg signal with the binary to decimal conversion, in preparation for the treatment)
    2011-04-06 19:57:14下载
    积分：1
  • Ritz_solution
    Ritz法解决-u +u=x 0<x<1，u(0)=0,u(1)=0的有限元边值问题。精确解为u(x)=1/(exp(1)-1/exp(1))*exp(-x)+1/(1/exp(1)-exp(1))*exp(x)+x，基函数为g(i)=sin(i*pi*x)(This matlab code is used to solve the boundary value problem of the differential equation: -u +u=x 0<x<1，u(0)=0,u(1)=0, where the accurate solution is : u(x)=1/(exp(1)-1/exp(1))*exp(-x)+1/(1/exp(1)-exp(1))*exp(x)+x)
    2011-05-30 20:14:35下载
    积分：1
  • Applied-Optimization-with-Matlab-Programming---P
    Applied Optimization with Matlab Programming - P. Venkataraman.pdf
    2013-03-27 07:29:20下载
    积分：1
  • gini
    一种常用的关于信号稀疏度测试的指标，值得拥有。(A commonly used test for signal sparsity indicators, worth having.)
    2011-11-23 22:15:53下载
    积分：1
  • lizilvbo
    粒子滤波算法，用来实现目标跟踪,多目标跟踪,以及目标跟踪定位等。(Particle filter algorithm is used for target tracking, multi-target tracking, and target tracking and positioning.)
    2013-05-19 22:49:45下载
    积分：1
  • 64677726moulation_classification
    基于高阶累积量的调制识别，可自己更改特征(Modulation recognition based on high-order cumulants can change its own characteristics.)
    2020-08-10 03:58:27下载
    积分：1
  • melfccfingerprint
    use the matlab tool to do the mel analysis in the audio fingerprinting system.(use the Matlab tool to do the analysis in mel the audio fingerprinting system.)
    2007-04-11 11:08:14下载
    积分：1
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