文件名称:neural-network
- 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：
- 2016-02-27
- 文件大小：
- 1.92mb
- 下载次数：
- 0次
- 提 供 者：
- 孙**
- 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
深度学习python实现，并附有MNIST上的测试程序，准确率98 以上-Deep learning learns low and high-level features large amounts of unlabeled data, improving classification on different, labeled, datasets. Deep learning can achieve an accuracy of 98 on the MNIST dataset.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
neural-network
..............\.gitignore
..............\deep
..............\....\deep.py
..............\....\display_network.py
..............\....\lazy_deep.py
..............\....\neurolib.py
..............\....\numerical_gradient.py
..............\....\sample_images.py
..............\....\selftaught.py
..............\....\softmax.py
..............\....\sparse_autoencoder.py
..............\display_network.py
..............\neurolib.py
..............\numerical_gradient.py
..............\pca
..............\...\display_network.py
..............\...\pca.py
..............\...\pca2d.py
..............\...\pca_gen.py
..............\...\sample_images.py
..............\rae
..............\...\.gitignore
..............\...\codeDataMoviesEMNLP
..............\...\...................\code
..............\...\...................\....\classifyWithRAE.m
..............\...\...................\....\computeCostAndGradRAE.m
..............\...\...................\....\forwardPropRAE.m
..............\...\...................\....\getAccuracy.m
..............\...\...................\....\getFeatures.m
..............\...\...................\....\getW.m
..............\...\...................\....\initializeParameters.m
..............\...\...................\....\RAECost.m
..............\...\...................\....\read_rtPolarity.m
..............\...\...................\....\soft_cost.m
..............\...\...................\....\trainTestRAE.m
..............\...\...................\....\tree2.m
..............\...\codeNIPS2011
..............\...\............\cell2str.m
..............\...\............\convertStanfordParserTrees.m
..............\...\............\getVectors.m
..............\...\............\reformatTree.m
..............\...\............\reorder.m
..............\...\............\run.m
..............\...\............\runReformatTree.m
..............\...\............\tree.m
..............\...\............\WordLookup.m
..............\...\display_network.py
..............\...\neurolib.py
..............\...\phrase2Vector.sh
..............\...\pyparse.py
..............\...\pypm.py
..............\...\sample_images.py
..............\...\scratch.py
..............\...\sparse_autoencoder.py
..............\...\stanford-parser-2011-09-14
..............\...\..........................\bin
..............\...\..........................\...\makeSerialized.csh
..............\...\..........................\...\run-tb-preproc
..............\...\..........................\build.xml
..............\...\..........................\conf
..............\...\..........................\....\atb-latest.conf
..............\...\..........................\....\ftb-latest.conf
..............\...\..........................\install.sh
..............\...\..........................\lexparser-gui.bat
..............\...\..........................\lexparser-gui.sh
..............\...\..........................\lexparser-lang-train-test.sh
..............\...\..........................\lexparser-lang.sh
..............\...\..........................\lexparser.bat
..............\...\..........................\lexparser.sh
..............\...\..........................\lexparser_lang.def
..............\...\..........................\Makefile
..............\...\..........................\ParserDemo.java
..............\...\..........................\ParserDemo2.java
..............\...\..........................\stanford-parser.jar
..............\...\treeparser.py
..............\README
..............\sample_images.py
..............\selftaught
..............\..........\display_network.py
..............\..........\neurolib.py
..............\..........\numerical_gradient.py
..............\..........\sample_images.py
..............\..........\selftaught.py
..............\..........\softmax.py
..............\..........\sparse_autoencoder.py
..............\..........\train_sparse_autoencoder_on_5to9.py
..............\softmax
..............\.......\display_network.py
..............\.......\numerical_gradient.py
..............\.......\sample_images.py
..............\.......\softmax.py
.....