January 2019
Beginner to intermediate
576 pages
14h 31m
Chinese
Content preview from 深度學習｜內行人的做法
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.
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第三章：深度網路基礎
RBM 的其他用途
以下是我們可以看到 RBM 在其他方面運用的例子：
• 降維 • 協同過濾（Collaborative filtering）
• 分類 • 主題模型化（Topic modeling）
• 迴歸
自動編碼器
我們也可以使用自動編碼器，來學習資料集的壓縮表達方式。通常我們會使用它來降低
資料集的維度。自動編碼器網路的輸出，就是輸入資料以最有效率的形式重建的結果。
與多層感知器的相似之處
自動編碼器與多層感知器神經網路具有很強的相似性，因為它們都具有輸入層、隱藏層
和輸出層。多層感知器網路的圖形（取自前面的章節）和自動編碼器的圖形，兩者之間
最關鍵的區別就是，自動編碼器的輸出層與輸入層具有相同的單元數量。
圖 3-7 呈現了一個自動編碼器網路的例子。
輸入層 瓶頸 網路重建結果
圖 3-7 自動編碼器網路架構
深度網路構建模塊
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除了輸出層之外，還有一些其他的區別，我們會在下一節進行簡要的說明。
自動編碼器的特徵定義
自動編碼器在許多方面與多層感知器有所不同：
• 它會在無監督學習中使用未標記資料。
• 它會構建出輸入資料的壓縮表達方式。
未標記資料的無監督學習。 自動編碼器可以直接根據未標記資料進行學習。這一點與
多層感知器和自動編碼器之間的第二個主要區別有關。 ...
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