{"id":6279,"date":"2017-07-20T16:28:27","date_gmt":"2017-07-20T07:28:27","guid":{"rendered":"https:\/\/deeplearning.jp\/?p=6279"},"modified":"2017-07-20T16:35:26","modified_gmt":"2017-07-20T07:35:26","slug":"6279-2","status":"publish","type":"post","link":"https:\/\/deeplearning.jp\/en\/6279-2\/","title":{"rendered":""},"content":{"rendered":"

Sorry, this entry is only available in \u65e5\u672c\u8a9e<\/a>. For the sake of viewer convenience, the content is shown below in the alternative language. You may click the link to switch the active language.<\/p>

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<\/th>\ndate<\/th>\ntitle<\/th>\ncontents<\/th>\n<\/tr>\n
1<\/td>\n2017\/4\/11<\/td>\nIntroduction<\/td>\nDeep Learning\u3068\u306f\/\u4eba\u5de5\u77e5\u80fd\u6280\u8853\u306e\u6b74\u53f2\u3001\u793e\u4f1a\u3078\u306e\u5f71\u97ff\/\u8b1b\u7fa9\u5168\u4f53\u50cf\u3068\u6ce8\u610f\u4e8b\u9805\/ \u5168\u54e1\u6328\u62f6<\/td>\n<\/tr>\n
2<\/td>\n2017\/4\/18<\/td>\nMachine Learning 1<\/td>\nPython\u3068\u7dda\u5f62\u4ee3\u6570\uff0c\u884c\u5217\u30fb\u30c6\u30f3\u30bd\u30eb\uff0c\u6570\u5f0f\u3068\u5b9f\u88c5\u306e\u30d6\u30ea\u30c3\u30b8<\/td>\n<\/tr>\n
3<\/td>\n2017\/4\/25<\/td>\nMachine Learning 2<\/td>\nk-NN, Logistic Regression, Softmax, train\/dev\/test\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8,\u5b66\u7fd2\u30d7\u30ed\u30bb\u30b9<\/td>\n<\/tr>\n
4<\/td>\n2017\/5\/2<\/td>\nPerceptron + Feed Forward Network, Gradient Descent<\/td>\nGradient Descent, MLP, Logistic Regression, Softmax<\/td>\n<\/tr>\n
5<\/td>\n2017\/5\/9<\/td>\nGradient Descent, Stochastic Gradient Descent, Optimizers<\/td>\nTensorFlow\u57fa\u790e\u3001\u5404\u7a2eOptimizers\u3001\u91cd\u307f\u306e\u521d\u671f\u5316\u306a\u3069<\/td>\n<\/tr>\n
6<\/td>\n2017\/5\/16<\/td>\nAutoencoders<\/td>\nDeep Learning\u3001\u7279\u5fb4\u62bd\u51fa\u3001Autoencoder\u6982\u8981\u3001dA, SdA, Sparse Coding, GPU<\/td>\n<\/tr>\n
7<\/td>\n2017\/5\/23<\/td>\nConvolutional Neural Networks(CNN)<\/td>\nCNN\u57fa\u790e\u3001\u7573\u8fbc\u307f\u3001\u30d7\u30fc\u30ea\u30f3\u30b0<\/td>\n<\/tr>\n
8<\/td>\n2017\/6\/6<\/td>\nConvolutional Neural Networks(CNN) 2<\/td>\n\u753b\u50cf\u51e6\u7406\u3001\u524d\u51e6\u7406\u3001Data Augmentation\u3001Batch Normalization\u3001\u53ef\u8996\u5316\u3001\u6700\u65b0\u52d5\u5411<\/td>\n<\/tr>\n
9<\/td>\n2017\/6\/13<\/td>\nRNN Basics<\/td>\n\u7cfb\u5217\u30c7\u30fc\u30bf, RNN<\/td>\n<\/tr>\n
10<\/td>\n2017\/6\/20<\/td>\nRNN and NLP<\/td>\nEmbedding, Projection, Word2vec, sequence-to-sequence<\/td>\n<\/tr>\n
11<\/td>\n2017\/6\/27<\/td>\nRNN, NLP, Image Processing<\/td>\nAttention, Image caption<\/td>\n<\/tr>\n
12<\/td>\n2017\/7\/4<\/td>\nSummary and Advanced Topics<\/td>\nAdvanced Topics<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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