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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超參數
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雖然我們在前面簡單談到了交叉熵的主題，並且提到對機率取對數可以把相乘變成相加
的關係，但我們沒有提到的是，對機率取對數的做法，就等於是把我們直接帶入資訊理
論和熵（entropy）概念的領域之中。
不同的理論基礎
儘管前面曾提到負對數可能性在數學上等價於交叉熵，但它們各自是以不
同的理論方法做為其基礎。
超參數
在機器學習領域有兩種參數，一種是模型最佳化過程中持續調整的參數，另一種則是我
們想讓網路訓練速度更快、效果更好而進行調整的參數。這些可調整的參數就是所謂的
「
超參數
（
hyperparameters
）」，它在學習演算法的訓練期間，負責控制最佳化函數和模
型的選擇。在 DL4J 中，我們把最佳化演算法也叫做「更新程序（updater）」，因為演算
法在整個權重空間中為了最小化誤差所採取的步驟，與「更新」是同義的。
超參數的選擇重點在於，必須確保模型面對訓練組資料時，既不會出現過度套入也不會
出現套入不足的情況，同時還希望能盡可能快一點學習到資料的結構。
學習速率
學習速率（learning rate）會影響最佳化過程中參數調整的幅度，以最大程度降低神經網
路推測值的誤差。它是一個係數，在更新程序橫掃過整個損失函數空間的過程中，就是
根據這個參數，對參數向量
x
每一步驟的調整（更新）幅度進行縮放調整。
在反向傳播過程中，我們會把誤差梯度乘以學習速率，然後再用這個相乘的結果，對連
結權重值前一次的迭代結果進行更新，以得出新的權重值。學習速率決定的是演算法下
一步驟要根據梯度做出多大幅度的調整。如果遇到比較大的誤差加上陡峭的梯度，搭配 ...
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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.