文件名称:DeepLearningTutorials-python
- 所属分类：
- 人工智能/神经网络/遗传算法
- 资源属性：
- [Linux] [SHELL] [源码]
- 上传时间：
- 2014-11-30
- 文件大小：
- 5.95mb
- 下载次数：
- 0次
- 提 供 者：
- w**
- 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
深度神经网络轻量级工具包，Python简单实现，内含各种模型的代码以及模型的简单理解说明，适合初学者阅读使用。-Deep Neural Networks lightweight toolkit, Python simple implementation, containing simple to understand explanation of each model and the model code, suitable for beginners read.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
DeepLearningTutorials-python
............................\.gitignore
............................\.hgignore
............................\.travis.yml
............................\code
............................\....\cA.py
............................\....\convolutional_mlp.py
............................\....\dA.py
............................\....\DBN.py
............................\....\hmc
............................\....\...\hmc.py
............................\....\...\test_hmc.py
............................\....\...\__init__.py
............................\....\logistic_cg.py
............................\....\logistic_sgd.py
............................\....\mlp.py
............................\....\rbm.py
............................\....\rnnrbm.py
............................\....\rnnslu.py
............................\....\SdA.py
............................\....\test.py
............................\....\utils.py
............................\data
............................\....\download.sh
............................\....\training_colorpatches_16x16_demo.mat
............................\doc
............................\...\.templates
............................\...\..........\layout.html
............................\...\conf.py
............................\...\contents.txt
............................\...\dA.txt
............................\...\DBN.txt
............................\...\deep.txt
............................\...\gettingstarted.txt
............................\...\hmc.txt
............................\...\images
............................\...\......\3wolfmoon.jpg
............................\...\......\3wolfmoon_output.png
............................\...\......\bm.png
............................\...\......\cnn_explained.png
............................\...\......\conv_1D_nn.png
............................\...\......\DBN3.png
............................\...\......\filters_at_epoch_14.png
............................\...\......\filters_corruption_0.png
............................\...\......\filters_corruption_30.png
............................\...\......\markov_chain.png
............................\...\......\mlp.png
............................\...\......\mnist_0.png
............................\...\......\mnist_1.png
............................\...\......\mnist_2.png
............................\...\......\mnist_3.png
............................\...\......\mnist_4.png
............................\...\......\mnist_5.png
............................\...\......\mylenet.png
............................\...\......\rbm.png
............................\...\......\rnnrbm.png
............................\...\......\rnnrbm.svg
............................\...\......\sample1.png
............................\...\......\sample2.png
............................\...\......\samples.png
............................\...\......\sparse_1D_nn.png
............................\...\intro.txt
............................\...\lenet.txt
............................\...\LICENSE.txt
............................\...\logreg.txt
............................\...\Makefile
............................\...\mlp.txt
............................\...\rbm.txt
............................\...\references.txt
............................\...\rnnrbm.txt
............................\...\rnnslu.txt
............................\...\scripts
............................\...\.......\docgen.py
............................\...\SdA.txt
............................\...\utilities.txt
............................\issues_closed
............................\.............\2_RBM_cost_fn.txt
............................\issues_open
............................\...........\1_SdA_performance.txt
............................\...........\3_RBM_scan_GPU.txt
............................\...........\4_RBM_scan.txt
............................\...........\5_results.txt
............................\...........\6_benchmarking_pybrain.txt
............................\misc
............................\....\do_nightly_build
............................\R