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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處理各類別數量不均衡的情況
|
303
隨機池化
（stochastic pooling
30
）
這是一種（特別針對）CNN 的正則化技術，我們可以使用一種隨機池化的形式來構
建 CNN 組合。每一次網路訓練都有可能會用到每個特徵映射圖的不同空間位置。
對抗型訓練
（adversarial training
31
）
你可以用一些比較小而且刻意挑選過形式比較差的訓練組資料，來建立一些輸入資
料。這樣就會生成另一個網路模型，它會以很高的可信度給出錯誤的答案。在實務
上，這已被證明可減少過度套入的情況，而且所得到的模型將具有抵抗不良樣本的
能力。
課程型學習
（curriculum learning
32
）
這種訓練的變形做法，會先從訓練組資料找出比較容易學習的樣本，來進行小批量
訓練，然後再逐步使用比較難學習的樣本進行訓練。採用這種策略可以加快訓練的
速度，讓結果收斂到更好的解，同時可充當網路的正則化器。以結果來看，這種做
法在某些領域相當成功。
平行化與正則化
你應該比較喜歡在學習期間，從參數向量的權重中去除雜訊，其中的理由
部分可歸因於學習速率的使用。如果想要消除最終權重內的一些雜訊，其
中一種方法就是針對好幾次的更新權重值取其平均。你也可以透過平行執
行模式中的參數平均效應，來達到這樣的效果。
在以平行方式執行的情況下，有時我們並不會使用在前函數來進行正則
化，因為參數平均本身就可以達到類似的效果。
處理各類別數量不均衡的情況
我們在機器學習中所用到的訓練資料集，每個類別的資料數量經常會多寡不一，明顯呈
現出各類別數量不均衡的情況 ...
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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.