April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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習題
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269
習題
1. 分群的定義是什麼？請指出幾種分群演算法。
2. 分群演算法的主要應用有哪些？
3. 指出在使用 K-Means 時，兩種選擇正確群聚數量的技術。
4. 什麼是標籤傳播？為什麼要做這件事？怎麼做？
5. 指出兩種可處理巨型資料組的分群演算法，以及兩種尋找高密度區域的演算法。
6. 指出適合使用主動學習的案例。如何實作它？
7. 異常檢測與新穎檢測有什麼不同？
8. 什麼是高斯混合？你可以用它來處理什麼工作？
9. 請指出使用高斯混合模型時，可找出正確群聚數量的兩種技術。
10. 經典的 Olivetti 臉譜資料組有 400 張 64
×
64 像素的灰階人臉照片。每一張照片都
被壓扁成大小為 4,096 的 1D 向量。資料組有 40 個人的照片（每人 10 張），大家
經常訓練模型來預測照片裡面是哪一個人。使用
sklearn.datasets.fetch_olivetti_
faces()
函式載入這個資料組，再將它拆成一個訓練組、一個驗證組，和一個測試組
（注意，這個資料組的尺度已被縮放在 0 與 1 之間了）。因為這個資料組很小，你
可能要使用分層抽樣來確保每個人在每一組裡面都有相同的照片數量。接下來使用
K-Means 來將照片分群，並確保你有良好的群聚數量（使用本章談過的技術）。將群
聚視覺化，你可以在每一個群聚中看到類似的臉譜嗎？
11. 繼續使用 Olivetti 臉譜資料組，訓練一個分類器來預測照片裡面的人是誰，並且用驗
證組來評估它。接著，使用
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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.