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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第七章：特定深度網路架構的調整
選定隱藏單元的數量
我們在嘗試選定 RBM 隱藏單元的數量時，主要關心的是如何避免出現過
度套入的情況。在這樣的約束條件下，我們可以採用一種直接把可見單元
乘以 0.75 的快速做法，來設定隱藏單元的數量。如果並沒有打算讓結果
呈現出很高的稀疏性，我們就會盡量避免使用較多的隱藏單元。如果輸
入資料集內包含大量非常相似的輸入資料，使用的參數數量還可以再少
一點。
採用不同類型的單元
通常我們碰到 RBM 或 DBN 時，其中的 RBM 所使用的多半是二元（binary，或邏輯）
單元。如果無法透過這種單元為資料訓練出很好的模型，我們還是有其他的選項，可用
於 RBM 的隱藏單元與可見單元。針對其他可使用的選項，我們在此列出多種不同類型
的單元：
• 多項式可見單元
• 高斯可見單元
• 二項式單元
• ReLU
多項式單元可用於主題（topic）模型化，也可用於構建推薦系統，或是用 RBM 來構建
分類器（在無監督預訓練之後，我們可以把輸出層放在最後一層進行分類，藉此構建出
分類器）。若與二元可見單元相比，多項式可見單元是運用 softmax 函數來取代邏輯函
數，並使用伯努利（Bernoulli）隱藏單元。
在處理圖片或語音時，我們發現二元可見單元只能提供比較差的特徵表達方式。在這種
情況下，我們就會使用高斯單元。不過，我們還是會繼續使用二元單元做為隱藏單元，
因為如果兩種單元都使用高斯單元，有可能導致訓練中的不穩定性變得更差。
在圖片和語音相關的應用中，我們之所以會使用高斯單元，主要是因為邏輯單元在這方 ...
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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.Addison B.
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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.