Rで Deep Learning
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http://www.okadajp.org/RWiki/?R%E3%81%A7+Deep+Learning
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COLOR(red){SIZE(30){Rで Deep Learning}} *パッケージ [#fac9d13b] **[[nnet: Feed-Forward Neural Networks and Multinomial Log-Linear Models:https://cran.r-project.org/web/packages/nnet/index.html]] [#k9f41ad7] **[[deepnet: deep learning toolkit in R:http://cran.r-project.org/web/packages/deepnet/index.html]] [#z347f4b7] **[[darch: Package for deep architectures and Restricted-Bolzmann-Machines:http://cran.r-project.org/web/packages/darch/index.html]] [#zaac4955] **[[deepr:https://github.com/woobe/deepr]] [#le2bc7fb] **[[h20:http://cran.r-project.org/web/packages/h2o/index.html]] [#b9ec665e] -[[Ｈ２Ｏを用いたディープラーニング。環境構築。:http://antena.tokyo/post-735]] -[[Rで一行でディープラーニング:http://d.hatena.ne.jp/dichika/20140503/p1]] **[[mxnet:https://mxnet.incubator.apache.org/]] [#h630d16d] -[[環境構築。:http://mxnet.io/get_started/install.html]] -[[Mxnetで回帰 #TokyoR 53th:https://www.slideshare.net/eguchiakifumi/mxnet-tokyor-53th]] -[[mxnetで頑張る深層学習 #TokyoR 57th:http://sssslide.com/www.slideshare.net/kashitan/20160924-tokyo-r57-66372506]] **[[tensorflow:https://cran.r-project.org/web/packages/tensorflow/index.html]] [#f5aeaf60] -[[環境構築。:https://tensorflow.rstudio.com/tensorflow/]] *事例 [#sc333f25] **[[Things to try after useR! – Part 1: Deep Learning with H2O:http://blenditbayes.blogspot.jp/2014/07/things-to-try-after-user-part-1-deep.html]] [#q895ed0a] **[[Deep Learning for R with MXNet useR! 2016 international R User conference:https://channel9.msdn.com/Events/useR-international-R-User-conference/useR2016/Deep-Learning-for-R-with-MXNet]] [#q03f0176] *書籍 [#wf27b6b5] -[[Rではじめる機械学習:https://www.amazon.co.jp/R%E3%81%A7%E3%81%AF%E3%81%98%E3%82%81%E3%82%8B%E6%A9%9F%E6%A2%B0%E5%AD%A6%E7%BF%92-%E3%83%87%E3%83%BC%E3%82%BF%E3%82%B5%E3%82%A4%E3%82%BA%E3%82%92%E6%8A%91%E3%81%88%E3%81%A6%E8%BB%BD%E9%87%8F%E3%81%AA%E7%92%B0%E5%A2%83%E3%81%A7%E6%94%BB%E7%95%A5%E6%B3%95%E3%82%92%E6%8E%A2%E3%82%8B-impress-top-gear/dp/4295002054/ref=sr_1_24?ie=UTF8&qid=1505658834]]
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COLOR(red){SIZE(30){Rで Deep Learning}} *パッケージ [#fac9d13b] **[[nnet: Feed-Forward Neural Networks and Multinomial Log-Linear Models:https://cran.r-project.org/web/packages/nnet/index.html]] [#k9f41ad7] **[[deepnet: deep learning toolkit in R:http://cran.r-project.org/web/packages/deepnet/index.html]] [#z347f4b7] **[[darch: Package for deep architectures and Restricted-Bolzmann-Machines:http://cran.r-project.org/web/packages/darch/index.html]] [#zaac4955] **[[deepr:https://github.com/woobe/deepr]] [#le2bc7fb] **[[h20:http://cran.r-project.org/web/packages/h2o/index.html]] [#b9ec665e] -[[Ｈ２Ｏを用いたディープラーニング。環境構築。:http://antena.tokyo/post-735]] -[[Rで一行でディープラーニング:http://d.hatena.ne.jp/dichika/20140503/p1]] **[[mxnet:https://mxnet.incubator.apache.org/]] [#h630d16d] -[[環境構築。:http://mxnet.io/get_started/install.html]] -[[Mxnetで回帰 #TokyoR 53th:https://www.slideshare.net/eguchiakifumi/mxnet-tokyor-53th]] -[[mxnetで頑張る深層学習 #TokyoR 57th:http://sssslide.com/www.slideshare.net/kashitan/20160924-tokyo-r57-66372506]] **[[tensorflow:https://cran.r-project.org/web/packages/tensorflow/index.html]] [#f5aeaf60] -[[環境構築。:https://tensorflow.rstudio.com/tensorflow/]] *事例 [#sc333f25] **[[Things to try after useR! – Part 1: Deep Learning with H2O:http://blenditbayes.blogspot.jp/2014/07/things-to-try-after-user-part-1-deep.html]] [#q895ed0a] **[[Deep Learning for R with MXNet useR! 2016 international R User conference:https://channel9.msdn.com/Events/useR-international-R-User-conference/useR2016/Deep-Learning-for-R-with-MXNet]] [#q03f0176] *書籍 [#wf27b6b5] -[[Rではじめる機械学習:https://www.amazon.co.jp/R%E3%81%A7%E3%81%AF%E3%81%98%E3%82%81%E3%82%8B%E6%A9%9F%E6%A2%B0%E5%AD%A6%E7%BF%92-%E3%83%87%E3%83%BC%E3%82%BF%E3%82%B5%E3%82%A4%E3%82%BA%E3%82%92%E6%8A%91%E3%81%88%E3%81%A6%E8%BB%BD%E9%87%8F%E3%81%AA%E7%92%B0%E5%A2%83%E3%81%A7%E6%94%BB%E7%95%A5%E6%B3%95%E3%82%92%E6%8E%A2%E3%82%8B-impress-top-gear/dp/4295002054/ref=sr_1_24?ie=UTF8&qid=1505658834]]
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