Supervised Sequence Labelling with Recurrent Neural Networks 筆記 -LSTM
Chapter 4 Long Short-Term Memory 4.1Network Architecture 乘法門允許LSTM存儲單元在長時間內存儲和訪問信息，從而減輕梯度消失的問題。例如，只要輸入門保持關閉(即有一個 激活接近於0)，單元的激活狀態不會被網絡中新的輸入所覆蓋。並且可以通過打開輸出門，把這個信息輸出給更後面的序列。（也就是說利用這種方法保持了單元的激活信息，給後面的序列用）L
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相關文章
- 1. BP,RNN 和 LSTM暨《Supervised Sequence Labelling with Recurrent Neural Networks-2012》閱讀筆記
- 2. Sequence Models(Week1)---Recurrent Neural Networks
- 3. CHAPTER 9 Sequence Processing with Recurrent Networks
- 4. Recurrent Neural Networks VS LSTM
- 5. Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
- 6. ROLO:Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
- 7. Paper：Sequence to Sequence Learning with Neural Networks
- 8. Sutskever2014_Sequence to Sequence Learning with Neural Networks
- 9. Sequence to Sequence Learning with Neural Networks學習筆記
- 10. Recurrent Neural Networks Tutorial閱讀筆記