[{"title":"( 26 个子文件 1003KB ) emd的matlab代码详解-RRN-MxNet:使用MxNet的循环神经网络","children":[{"title":"RRN-MxNet-master","children":[{"title":"images","children":[{"title":"KFC_Thinking01.jpg <span style='color:#111;'> 797.75KB </span>","children":null,"spread":false},{"title":"ffn_rnn.png <span style='color:#111;'> 20.59KB </span>","children":null,"spread":false},{"title":"unroll_input.png <span style='color:#111;'> 3.19KB </span>","children":null,"spread":false},{"title":"RNN2.png <span style='color:#111;'> 13.98KB </span>","children":null,"spread":false},{"title":"batch.png <span style='color:#111;'> 2.52KB </span>","children":null,"spread":false},{"title":"martians-chart5_preview.jpeg <span style='color:#111;'> 51.04KB </span>","children":null,"spread":false},{"title":"seq2.png <span style='color:#111;'> 12.34KB </span>","children":null,"spread":false},{"title":"batch4.png <span style='color:#111;'> 7.17KB </span>","children":null,"spread":false},{"title":"batch3.png <span style='color:#111;'> 7.40KB </span>","children":null,"spread":false},{"title":"unRolled_rnn.png <span style='color:#111;'> 39.32KB </span>","children":null,"spread":false},{"title":"lstm.png <span style='color:#111;'> 16.61KB </span>","children":null,"spread":false},{"title":"martians-chart5_preview4.jpg <span style='color:#111;'> 72.07KB </span>","children":null,"spread":false},{"title":"sequene_to_sequence.png <span style='color:#111;'> 23.48KB </span>","children":null,"spread":false},{"title":"RNN.png <span style='color:#111;'> 13.73KB </span>","children":null,"spread":false},{"title":"GAN_SAMPLE.png <span style='color:#111;'> 13.58KB </span>","children":null,"spread":false},{"title":"cnnvsrnn.png <span style='color:#111;'> 7.13KB </span>","children":null,"spread":false},{"title":"GAN_Model.png <span style='color:#111;'> 28.66KB </span>","children":null,"spread":false},{"title":"batch_reshape.png <span style='color:#111;'> 14.90KB </span>","children":null,"spread":false},{"title":"humans_mars.png <span style='color:#111;'> 14.21KB </span>","children":null,"spread":false},{"title":"GAN_image.png <span style='color:#111;'> 92.94KB </span>","children":null,"spread":false},{"title":"batch2.png <span style='color:#111;'> 2.92KB </span>","children":null,"spread":false},{"title":"loss.png <span style='color:#111;'> 24.58KB </span>","children":null,"spread":false}],"spread":false},{"title":"Test-rnn.ipynb <span style='color:#111;'> 50.22KB </span>","children":null,"spread":false},{"title":".ipynb_checkpoints","children":[{"title":"sumbission-checkpoint.ipynb <span style='color:#111;'> 31.18KB </span>","children":null,"spread":false},{"title":"Test-rnn-checkpoint.ipynb <span style='color:#111;'> 31.18KB </span>","children":null,"spread":false}],"spread":true},{"title":"README.md <span style='color:#111;'> 27.50KB </span>","children":null,"spread":false}],"spread":true}],"spread":true}]