登录
首页 » WINDOWS » SparkMLlibDeepLearn-master

SparkMLlibDeepLearn-master

于 2020-11-26 发布 文件大小:293KB
0 194
下载积分: 1 下载次数: 2

代码说明:

  深度信念网络,非常好的代码,有具体的事例(Deep belief network, very good code, there are specific examples)

文件列表:

SparkMLlibDeepLearn-master
SparkMLlibDeepLearn-master\.cache
SparkMLlibDeepLearn-master\.classpath
SparkMLlibDeepLearn-master\.project
SparkMLlibDeepLearn-master\.settings
SparkMLlibDeepLearn-master\.settings\org.eclipse.jdt.core.prefs
SparkMLlibDeepLearn-master\LICENSE
SparkMLlibDeepLearn-master\README.md
SparkMLlibDeepLearn-master\bin
SparkMLlibDeepLearn-master\bin\CAE
SparkMLlibDeepLearn-master\bin\CAE\CAE$.class
SparkMLlibDeepLearn-master\bin\CAE\CAE.class
SparkMLlibDeepLearn-master\bin\CNN
SparkMLlibDeepLearn-master\bin\CNN\CNN$.class
SparkMLlibDeepLearn-master\bin\CNN\CNN.class
SparkMLlibDeepLearn-master\bin\DBN
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$16.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$DBNtrain$1$$anonfun$apply$mcVI$sp$1.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$DBNtrain$1.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$DBNtrain$2$$anonfun$2.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$DBNtrain$2$$anonfun$apply$mcVI$sp$2$$anonfun$apply$mcVI$sp$3.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$DBNtrain$2$$anonfun$apply$mcVI$sp$2.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$DBNtrain$2.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$InitialW$1.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$Initialb$1.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$Initialc$1.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$InitialvW$1.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$Initialvb$1.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$Initialvc$1.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$RBMtrain$1$$anonfun$1.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$RBMtrain$1$$anonfun$apply$mcVI$sp$4$$anonfun$10.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$RBMtrain$1$$anonfun$apply$mcVI$sp$4$$anonfun$11.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$RBMtrain$1$$anonfun$apply$mcVI$sp$4$$anonfun$12.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$RBMtrain$1$$anonfun$apply$mcVI$sp$4$$anonfun$13.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$RBMtrain$1$$anonfun$apply$mcVI$sp$4$$anonfun$14.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$RBMtrain$1$$anonfun$apply$mcVI$sp$4$$anonfun$15.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$RBMtrain$1$$anonfun$apply$mcVI$sp$4$$anonfun$3.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$RBMtrain$1$$anonfun$apply$mcVI$sp$4$$anonfun$4.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$RBMtrain$1$$anonfun$apply$mcVI$sp$4$$anonfun$5.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$RBMtrain$1$$anonfun$apply$mcVI$sp$4$$anonfun$6.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$RBMtrain$1$$anonfun$apply$mcVI$sp$4$$anonfun$7.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$RBMtrain$1$$anonfun$apply$mcVI$sp$4$$anonfun$8.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$RBMtrain$1$$anonfun$apply$mcVI$sp$4$$anonfun$9.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$RBMtrain$1$$anonfun$apply$mcVI$sp$4.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$$anonfun$RBMtrain$1.class
SparkMLlibDeepLearn-master\bin\DBN\DBN$.class
SparkMLlibDeepLearn-master\bin\DBN\DBN.class
SparkMLlibDeepLearn-master\bin\DBN\DBNConfig$.class
SparkMLlibDeepLearn-master\bin\DBN\DBNConfig.class
SparkMLlibDeepLearn-master\bin\DBN\DBNModel$$anonfun$dbnunfoldtonn$1.class
SparkMLlibDeepLearn-master\bin\DBN\DBNModel.class
SparkMLlibDeepLearn-master\bin\DBN\DBNweight$.class
SparkMLlibDeepLearn-master\bin\DBN\DBNweight.class
SparkMLlibDeepLearn-master\bin\NN
SparkMLlibDeepLearn-master\bin\NN\NNConfig$.class
SparkMLlibDeepLearn-master\bin\NN\NNConfig.class
SparkMLlibDeepLearn-master\bin\NN\NNLabel$.class
SparkMLlibDeepLearn-master\bin\NN\NNLabel.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$1.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$11$$anonfun$12.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$11.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$13.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$14$$anonfun$5.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$14.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$15$$anonfun$apply$1.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$15.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$16.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$17.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$2.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$21.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$22$$anonfun$apply$2$$anonfun$6.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$22$$anonfun$apply$2$$anonfun$7.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$22$$anonfun$apply$2.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$22$$anonfun$apply$3.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$22.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$23.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$24$$anonfun$apply$4.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$24.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$25$$anonfun$apply$5.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$25.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$26.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$27.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$28.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$3.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$ActiveP$1$$anonfun$18.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$ActiveP$1$$anonfun$19.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$ActiveP$1$$anonfun$20.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$ActiveP$1.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$InitialActiveP$1.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$InitialWeight$1.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$InitialWeightV$1.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$NNapplygrads$1.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$NNbp$1.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$NNbp$2.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$NNtrain$1$$anonfun$4.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$NNtrain$1$$anonfun$apply$mcVI$sp$1$$anonfun$10.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$NNtrain$1$$anonfun$apply$mcVI$sp$1$$anonfun$6$$anonfun$apply$2$$anonfun$2.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$NNtrain$1$$anonfun$apply$mcVI$sp$1$$anonfun$6$$anonfun$apply$2.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$NNtrain$1$$anonfun$apply$mcVI$sp$1$$anonfun$7.class
SparkMLlibDeepLearn-master\bin\NN\NeuralNet$$anonfun$NNtrain$1$$anonfun$apply$mcVI$sp$1$$anonfun$8.class

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • grey-pid
    灰色pid,离散灰色系统,连续灰色系统,pid控制,灰色预测(gray pid, discrete gray, gray row, pid control, Gray forecast)
    2007-06-09 12:02:45下载
    积分:1
  • gaosi
    基于MATLAB的处理图像的差分高斯滤波的部分代码,需要的同志请下载。(MATLAB code based on partial differential image processing Gaussian filtering, comrades need to download.)
    2015-03-26 10:43:08下载
    积分:1
  • matlab-HYPER-VLOLUME
    MATLAB 第三代多目标性能指标计算源程序,适合于较新的多目标智能算法比较(MATLAB source the third generation of multi-objective performance index calculation, suitable for a new multi-objective intelligent algorithm)
    2015-04-09 20:29:21下载
    积分:1
  • [matlab]altificialbeecolonyalgorithm
    人工蜂群算法的matlab源代码,共有两个版本,原作者版和改进版。 (Artificial Bee Colony (ABC)algorithm matlab source code, there are two versions, the original author version and an improved version. Artificial Bee Colony (ABC) is one of the most recently defined algorithms by Dervis Karaboga in 2005, motivated by the intelligent behavior of honey bees. )
    2020-11-17 18:29:41下载
    积分:1
  • j
    说明:  基于Labview与Matlab混合编程的应用研究实例(Examples of applied research based on Labview and Matlab)
    2013-03-27 18:58:51下载
    积分:1
  • mainDensityClust.m副本
    该编程源代码主要介绍了一种密度峰值聚类的方法(This programming source code mainly introduces a method of density peak clustering.)
    2018-12-18 11:14:02下载
    积分:1
  • Wind_and_guangfu_flow
    说明:  IEEE33节点的并网多种分布式发电可直接运行(Ieee33 grid connected distributed generation can run directly)
    2020-05-27 20:56:26下载
    积分:1
  • MHH
    本程序是求解神经电HH方程的较为简单的例子。可以帮助大家了解神经放电的问题(This procedure is to solve HH equation ENoG more simple example. Can help everyone understand the problem of nerve discharge)
    2007-07-29 21:06:31下载
    积分:1
  • OFM_s
    generation de modulation OFDM
    2009-07-17 17:23:26下载
    积分:1
  • matlabpicMRF
    一个马尔可夫(MRF)图像分割MATLAB的源码,有30几个函数。Markov随机场的例子程序,对于初学入门MRF的人很有用,能得到直观的印象。(A Markov (MRF) image segmentation MATLAB source code, 30 a few functions. Markov random sample program, for beginners who are useful entry-MRF can get a visual impression.)
    2011-04-19 11:38:33下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载