文件名称:bergpalm_DeepLearnToolbox
- 所属分类：
- 人工智能/神经网络/遗传算法
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2016-06-01
- 文件大小：
- 14.09mb
- 下载次数：
- 0次
- 提 供 者：
- 王*
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
如今深度学习是AI和机器学习领域最热门的学习趋势。这是为深度学习而开发的matlab工具箱，很适合大家。-Today, deep learning is AI machine learning and learning the hottest trend. This is for deep learning and the development of matlab toolbox is suitable for everyone.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
license.txt
rasmusbergpalm-DeepLearnToolbox-45ef96c
.......................................\CAE
.......................................\...\caeapplygrads.m
.......................................\...\caebbp.m
.......................................\...\caebp.m
.......................................\...\caedown.m
.......................................\...\caeexamples.m
.......................................\...\caenumgradcheck.m
.......................................\...\caesdlm.m
.......................................\...\caetrain.m
.......................................\...\caeup.m
.......................................\...\max3d.m
.......................................\...\scaesetup.m
.......................................\...\scaetrain.m
.......................................\CNN
.......................................\...\cnnapplygrads.m
.......................................\...\cnnbp.m
.......................................\...\cnnexamples.m
.......................................\...\cnnff.m
.......................................\...\cnnnumgradcheck.m
.......................................\...\cnnsetup.m
.......................................\...\cnntest.m
.......................................\...\cnntrain.m
.......................................\DBN
.......................................\...\dbnexamples.m
.......................................\...\dbnsetup.m
.......................................\...\dbntrain.m
.......................................\...\dbnunfoldtonn.m
.......................................\...\rbmdown.m
.......................................\...\rbmtrain.m
.......................................\...\rbmup.m
.......................................\NN
.......................................\..\nnapplygrads.m
.......................................\..\nnbp.m
.......................................\..\nnchecknumgrad.m
.......................................\..\nnexamples.m
.......................................\..\nnff.m
.......................................\..\nnsetup.m
.......................................\..\nntest.m
.......................................\..\nntrain.m
.......................................\README.md
.......................................\REFS.md
.......................................\SAE
.......................................\...\saeexamples.m
.......................................\...\saesetup.m
.......................................\...\saetrain.m
.......................................\data
.......................................\....\mnist_uint8.mat
.......................................\tests
.......................................\.....\runalltests.m
.......................................\.....\test_nn_gradients_are_numerically_correct.m
.......................................\util
.......................................\....\allcomb.m
.......................................\....\expand.m
.......................................\....\flicker.m
.......................................\....\flipall.m
.......................................\....\fliplrf.m
.......................................\....\flipudf.m
.......................................\....\im2patches.m
.......................................\....\makeLMfilters.m
.......................................\....\patches2im.m
.......................................\....\randcorr.m
.......................................\....\randp.m
.......................................\....\rnd.m
.......................................\....\sigm.m
.......................................\....\sigmrnd.m
.......................................\....\softmax.m
.......................................\....\visualize.m
.......................................\....\whiten.m
.......................................\....\xunit
.......................................\....\.....\+xunit
.......................................\....\.....\......\+utils
.......................................\....\.....\......\......\Contents.m
.......................................\....\.....\......\......\arrayToString.m
.....