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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第五章：打造深度網路
• 時間序列資料模型化
• 自然語言處理應用
本章會用到許多深度網路的概念，也就是我們從第 1 章開始就一直在建立的相關概念。
雖然我們無法只用一個範例來涵蓋第 4 章介紹過的每一種架構，但我們確實編寫了一整
組範例，希望能讓你更深入了解深度學習的核心概念，這樣你就可以更容易把大多數範
例擴展到新的應用目標上了。我們首先來重新檢視一下之前介紹過的各種類型資料，如
何對應到合適的網路架構。
在網路上尋找範例
我們已經在 GitHub 儲存庫（
http://bit.ly/2uy8ClX
）創建了一個範例的 fork
副本。因為像 DL4J 這樣的專案，會一直隨時間不斷發展，因此我們希望
為讀者提供一個能夠與本書版本保持一致的程式碼快照。
欄位型資料與多層感知器
一般的欄位型資料具有靜態的結構，而且只要透過 DL4J 中的經典多層感知器神經網
路，就能達到最好的模型化效果。如果稍微進行一些特徵工程的工作，對於這些問題也
許有些幫助，但我們通常會讓網路自己針對資料集找出最好的權重組合。使用多層感知
器進行模型化時，超參數調整是主要的挑戰之一。我們會在第 6 章介紹各種技術，協助
你選出適當的超參數。
圖片資料與 CNN
CNN（卷積神經網路）已被證明特別擅長從原始圖片資料找出其中的結構。在過去，
圖片模型化領域一直被過量的預處理技術所主導，總是要先設法讓輸入圖片具備一定程
度的一致性，再轉換成模型化技術更容易處理的形式。只要是稍微旋轉或縮放的輕微變
化，就有可能讓圖片處理變成一項艱鉅的任務。 ...
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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.