RNN模型与NLP应用.zip

[{"title":"（ 12 个子文件 272.08MB ） RNN模型与NLP应用.zip","children":[{"title":"【自然语言处理】RNN模型与NLP应用 by Shusen Wang_P5_005 - RNN模型与NLP应用(5)：多层RNN、双向RNN、预训练.flv <span style='color:#111;'> 18.29MB </span>","children":null,"spread":false},{"title":"【自然语言处理】RNN模型与NLP应用 by Shusen Wang_P2_002 - RNN模型与NLP应用(2)：文本处理与词嵌入.flv <span style='color:#111;'> 26.31MB </span>","children":null,"spread":false},{"title":"【自然语言处理】RNN模型与NLP应用 by Shusen Wang_P10_010 - Transformer模型(1) 剥离RNN，保留Attention.flv <span style='color:#111;'> 37.42MB </span>","children":null,"spread":false},{"title":"【自然语言处理】RNN模型与NLP应用 by Shusen Wang_P7_007 - RNN模型与NLP应用(7)：机器翻译与Seq2Seq模型.flv <span style='color:#111;'> 28.61MB </span>","children":null,"spread":false},{"title":"【自然语言处理】RNN模型与NLP应用 by Shusen Wang_P9_009 - RNN模型与NLP应用(9)：Self-Attention (自注意力机制).flv <span style='color:#111;'> 10.16MB </span>","children":null,"spread":false},{"title":"【自然语言处理】RNN模型与NLP应用 by Shusen Wang_P12_012 - BERT (预训练Transformer模型).flv <span style='color:#111;'> 16.00MB </span>","children":null,"spread":false},{"title":"【自然语言处理】RNN模型与NLP应用 by Shusen Wang_P1_001 - RNN模型与NLP应用(1)：数据处理基础.flv <span style='color:#111;'> 16.59MB </span>","children":null,"spread":false},{"title":"【自然语言处理】RNN模型与NLP应用 by Shusen Wang_P3_003 - RNN模型与NLP应用(3)：Simple RNN模型.flv <span style='color:#111;'> 32.22MB </span>","children":null,"spread":false},{"title":"【自然语言处理】RNN模型与NLP应用 by Shusen Wang_P11_011 - Transformer模型(2) 从Attention层到Transformer网络.flv <span style='color:#111;'> 22.07MB </span>","children":null,"spread":false},{"title":"【自然语言处理】RNN模型与NLP应用 by Shusen Wang_P6_006 - RNN模型与NLP应用(6)：Text Generation (自动文本生成).flv <span style='color:#111;'> 39.16MB </span>","children":null,"spread":false},{"title":"【自然语言处理】RNN模型与NLP应用 by Shusen Wang_P8_008 - RNN模型与NLP应用(8)：Attention (注意力机制).flv <span style='color:#111;'> 25.75MB </span>","children":null,"spread":false},{"title":"【自然语言处理】RNN模型与NLP应用 by Shusen Wang_P4_004 - RNN模型与NLP应用(4)：LSTM模型.flv <span style='color:#111;'> 19.76MB </span>","children":null,"spread":false}],"spread":true}]