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DeepLearnToolbox-master

于 2021-03-21 发布
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下载积分: 1 下载次数: 8

代码说明:

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

  • durbin
    这是个用Durbin递归求预测误差滤波器和他的输出,是电子所邹谋炎老师给出的例程。(This is a demand by Durbin recursive prediction error filter, and his output is of Electronics Zou Mou-Yan teachers given routine.)
    2007-07-20 11:07:05下载
    积分:1
  • SA
    说明:  用matalab模拟退火算法解决 10 个城市的 TSP 问题,程序详细、实用,很适合初学者。(Using simulated annealing algorithm to solve matalab 10 cities TSP problem, the program detailed, practical, very suitable for beginners.)
    2014-09-08 10:10:24下载
    积分:1
  • km
    说明:  kmeans算法,用于聚类,简单matlabcode(kmeans code)
    2013-03-19 00:42:33下载
    积分:1
  • DTCDPE
    直接转矩控制无刷直流电机的matlab仿真程序,PI闭环调节(Direct torque control brushless DC motor matlab simulation program, PI closed loop control )
    2021-01-11 17:38:48下载
    积分:1
  • h4psk
    基于MATLAB的QPSK过程编程,并且对其过程的信号流程进行分析,用星座图分析其误码率(The QPSK based on MATLAB programming process, and its process of signal flow analysis, using the constellation diagram to analyze the bit error rate)
    2008-04-23 22:13:44下载
    积分:1
  • mmse
    MMSE解相关多用户检测器和CD传统多用户检测器的误码率比较(八个用户)程序( MMSE decorrelating multiuser detector and CD traditional multi-user detector error rate (eight users) procedure)
    2013-04-13 23:12:57下载
    积分:1
  • SAR-IMAGING---
    成像处理一般包括三个步骤:1、距离向脉冲压缩,对一个合成孔径内的每个回波脉冲均进行压缩;2、距离徙动矫正,把属于同一点目标的脉冲校正到等距离线上、3、方位向脉冲压缩。(Imaging process generally comprises three steps: a distance to the pulse compression of echo pulses each have an inner aperture compression 2, range migration correction, the pulses belonging to the same point in the correction target line equidistant , 3, azimuth compression to pulse.)
    2013-12-07 13:23:27下载
    积分:1
  • music
    谱估计的music算法,比较典型的谱估计方法,研究了半天才写出来的哦(The music spectrum estimation algorithm, a typical spectral estimation method to study for a long time to write it out, oh)
    2009-04-20 22:14:48下载
    积分:1
  • DEM
    demmarage asynchrone
    2010-12-29 21:36:06下载
    积分:1
  • Synthetic-Aperture-Radar-Imaging-Simulated-in-MAT
    synthetic aperture radar simulation
    2012-08-11 18:44:56下载
    积分:1
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