April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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物體偵測
|
475
此外還有一些創新可能是你感興趣的
，
例如使用跳接來恢復一些在
CNN
中遺失的空間解
析度
（
很快就會在探討語義分割時介紹
）。
在
2016
年的論文中
，
作者提出一種使用階層式
分類的
YOLO9000
模型
：
這個模型可以預測在一個稱為
WordTree
的視覺階層中的各個節點
的機率
，
所以網路可以很有信心地預測一張圖像代表
（
例如
）
一隻狗
，
即使它不確定是哪
一種狗
。
我希望你閱讀這三篇論文
，
它們不但很有趣
，
也舉一些很棒的例子來告訴你如何
逐漸改善深度學習系統
。
mean Average Precision（mAP）
mean
Average
Precision
（mAP）
是經常在物體偵測任務中使用的評量標準
。「 mean
Average」
聽起來有點多餘
，
不是嗎
？
為了說明這個標準
，
我們回到第
3
章討
論過的兩種分類標準
：precision
和
recall。
之前提過兩者之間的平衡
：recall
越
高
，precision
就越低
。
你可以用
precision/recall
曲線來將這種情形視覺化
（
見圖
3-5）。
我們可以計算這個曲線的曲線下方區域面積
（AUC）
來將它歸納成一個數
字
。
但
precision/recall
曲線可能有一些部分是
precision
隨著
recall
的增加而上升
的
，
尤其是在
recall
值很低時
（
你可以在圖
3-5
的左上方看到這個情形
）。
這就是
mAP
標準的動機之一
。
假設分類器在
10% recall
時有
90% precision，
但是在
20% recall
時有
96%
precision，
此時完全不需要取捨 ...
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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.