April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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Kernel PCA
|
221
它很適合用來處理大型的訓練組
，
以及線上執行
PCA（
也就是在新實例到達時立刻
處理
）。
下面的程式將
MNIST
資料組拆成
100
個小批次
（
使用
NumPy
的
array_split()
函式
），
並
將它們送給
Scikit-Learn
的
IncrementalPCA
類別
（https://homl.info/32）
5
，
來將
MNIST
資
料組的維數降成
154
維
（
跟之前一樣
）。
注意
，
你必須用各個小批次來呼叫
partial_fit()
方法
，
而不是用整個訓練組來呼叫
fit()
方法
：
from sklearn.decomposition import
IncrementalPCA
n_batches = 100
inc_pca = IncrementalPCA(n_components=154)
for
X_batch
in
np.array_split(X_train, n_batches):
inc_pca.partial_fit(X_batch)
X_reduced = inc_pca.transform(X_train)
你也可以使用
NumPy
的
memmap
類別來處理二進制檔案或磁碟內的大型陣列
，
就像它們完
全在記憶體一樣
，
這個類別只會將它需要的資料載入記憶體
。
因為
IncrementalPCA
類別
在任何時間都只使用小部分的陣列
，
記憶體的使用量在控制之下
，
所以你可以呼叫一般的
fit()
方法
，
就像這樣
：
X_mm = np.memmap(filename, dtype="float32", ...
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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.