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深度學習|內行人的做法
book

深度學習|內行人的做法

by Josh Patterson, Adam Gibson
January 2019
Beginner to intermediate
576 pages
14h 31m
Chinese
GoTop Information, Inc.
Content preview from 深度學習|內行人的做法
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第三章:深度網路基礎
遞迴神經網路RNN。 遞迴神經網路已被證實可用於字元序列模型化,並可用來生
成清晰連貫的新序列。我們在 5 可以看到一個遞迴神經網路的例子,其中我們針對
莎士比亞所有作品進行模型化,然後用來產生新的莎士比亞風格文字。
遞迴神經網路另一個有趣的應用是來自 Lipton Elkan 的相關工作,他們針對諸如
Coors Light」這類專有名詞和其他啤酒領域的行話,運用網路建立相應的模型。然後
我們就可以透過提示來引導,生成關於啤酒的評論(例如「給我一個德國啤酒的三星評
論」),結果頗令人印象深刻。以下就是該程式所生成的啤酒評論樣本:
酒吧裡常見的一種啤酒。顏色是漂亮的暗紅色,杯子上頭會留有很多酒花。本
身具有覆盆子和巧克力的香氣。雖然有覆盆子的感覺,但味道並不算濃。波旁
酒(
bourbon
)也很微妙。我實在不知道該怎麼歸類這種啤酒的口味。如果多來
點碳化的感覺,也許我會更偏愛一些。這啤酒還算好喝,但如果喝不到我也不
會太介意
8
深度學習之道
如今深度學習這個領域備受矚目,因此存在許多行銷炒作的說法,這倒也不是什麼奇怪
的事。然而,深度學習終究還是希望能回答像是「這圖片是一張人的臉嗎?」這種基本
的機器學習問題。不同之處在於,深度學習採用了前一代的神經網路技術,並加入先進
的自動化特徵結構,使一些複雜資料的計算難題變得更容易回答。
身為一個深度學習專業工作者,如果想要展現你的能力,最好的方式就是能夠為你的輸
入資料找出最適當的深度網路架構。如果你把這件事做得很好,就可以運用全新而有趣 ...
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Publisher Resources

ISBN: 9789865020262