Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
預備知識： RNN：循環神經網絡 處理序列化數據，一般用於多輸入多輸出，數據之間存在關聯性 U：輸入層到隱藏層的權重矩陣 V：隱藏層到輸出層的權重矩陣 W：隱藏層S不僅僅取決於當前這次的的輸入x，還取決於上一次隱藏層的值St-1，權重矩陣W就是隱藏層上一次的值作爲這一次輸入的權重。 用公式表示如下： Ot=g(V*St) St=f(U*Xt+
相關文章
- 1. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
- 2. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation之每日一篇
- 3. 翻譯：Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
- 4. Paper：Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- 5. Learning Phrase Representations using RNN Encoder–Decoder for...
- 6. 2014-Learning Phrase Representations using RNN Encoder–Decoder
- 7. (24) GRU & S2S：Learning Phrase Representations using RNN Encoder–Decoder for SMT
- 8. Statistical Machine Translation Tutorial Reading
- 9. Seq2Seq系列（一）：RNN Encoder-Decoder
- 10. Neural Machine Translation by Jointly Learning to Align and....
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相關文章
- 1. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
- 2. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation之每日一篇
- 3. 翻譯：Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
- 4. Paper：Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- 5. Learning Phrase Representations using RNN Encoder–Decoder for...
- 6. 2014-Learning Phrase Representations using RNN Encoder–Decoder
- 7. (24) GRU & S2S：Learning Phrase Representations using RNN Encoder–Decoder for SMT
- 8. Statistical Machine Translation Tutorial Reading
- 9. Seq2Seq系列（一）：RNN Encoder-Decoder
- 10. Neural Machine Translation by Jointly Learning to Align and....