BP,RNN 和 LSTM暨《Supervised Sequence Labelling with Recurrent Neural Networks-2012》閱讀筆記
一、BackPropagation ：表示第層第k個神經元到第層第j個神經元的連接權重； ：表示第層第j個神經元的偏置； ：表示第層第j個神經元的帶權輸入； ：表示第層第j個神經元的激活值； ：表示一個激活函數(sigmoid,relu,tanh)； 向量化上面的公式： L(W,b,x,y)：表示損失函數，其中y是正確值，x是輸入，下面是二次方損失函數的計算； 阿達馬（Hadamard）乘積，表示
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相關文章
- 1. Supervised Sequence Labelling with Recurrent Neural Networks 筆記 -LSTM
- 2. Recurrent Neural Networks Tutorial閱讀筆記
- 3. 《Sequence to Sequence Learning with Neural Networks》閱讀筆記
- 4. 論文閱讀：《Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neu》
- 5. <A Critical Review of Recurrent Neural Networks for Sequence Learning>閱讀筆記
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- 8. 【論文閱讀筆記】Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
- 9. 【論文閱讀】Sequence to Sequence Learning with Neural Networks
- 10. Document Modeling with Gated Recurrent Neural Network for Sentiment Classification 論文閱讀筆記