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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對於問題的構思
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9
對於問題的構思
如果想更了解如何套用機器學習的概念，一開始就必須先懂得如何提出正確的問題。以
下就是我們必須定義的一些東西：
• 我們（模型）想提取資訊的輸入資料究竟是什麼？
• 這些資料最適合的是什麼模型？
• 我們想從模型的新資料中得出什麼樣的答案？
如果我們能夠好好回答這三個問題，就可以建立一個機器學習工作流程，打造出我們的
模型，並生成我們所想要的答案。為了給這個工作流程提供更好的支援，我們會先回顧
一下機器學習實務上特別需要注意的一些核心概念。隨後我們就會回頭把機器學習的這
些概念結合起來，然後運用這些認知，對神經網路和深度學習建立更深入的理解。
機器學習背後的數學基礎：線性代數
線性代數是機器學習和深度學習的重要基礎。線性代數提供了數學基礎，讓我們用來打
造模型的方程式能夠得以求解。
James E.Gentle 的《
矩陣代數
：
理論
、
計算與統計應用
（
Matrix Algebra:
Theory, Computations, and Applications in Statistics
，
http://mason.gmu.edu/~jgentle/
books/matbk/index.htm
）》 是一本很好的線性代數著作。
我們先來看看這個領域的一些核心概念，首先要介紹的是叫做「
純量
」的基本概念。
純量
在數學這個領域中，只要提到純量（scalar）這個術語，就表示我們關心的是向量中的
元素值（element）。純量是一個實數值，它可用來定義向量空間（vector space ...
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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.Julian F.
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.
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.Amir M.
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.