July 2019
Intermediate to advanced
365 pages
8h 13m
Chinese
Content preview from Python机器学习手册：从数据预处理到深度学习
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301
第 20 章
神经网络
20.0
简介
神经网络的核心是
神经元
（
unit
）。神经元接收一个或者多个输入，为每个输入乘以一个
参数（又叫作
权重
），接着对加权之后的输入值求和再加上某个偏差值（一般是
1
）， 最
后把这个值反馈给一个激活函数。这个输出值会被向前传递给神经网络中更深层的神经
元（如果还有更深层的神经元的话）。
前馈
（
feedforward
）神经网络，又叫作
多层感知器
（
multilayer perceptron
），是现实世
界中最简单的人工神经网络。神经网络可以视为由一系列相互连接的层组成的网络，它
的一端连接着一个观察值的特征值，另一端连接着对应的目标值（比如观察值的分类）。
观察值的特征值在网络中向前传播，每经过一层，网络都会对特征值进行转换，目标是
让最后的输出与目标值相同，“前馈”这个名字就源于此。
具体来说，前馈神经网络包含
3
种类型的神经元层。神经网络的起始端有一个输入层，
输入层的每一个神经元包含一个观察值的某一个特征值。比如，如果一个观察值有
100
个特征，那么输入层就有
100
个节点。神经网络的末端有一个输出层，它把隐藏层的输
出转换成对我们的任务有用的值。举个例子，如果我们的目标是对数据做二元分类，我
们可以使用一个只有一个节点的输出层，这个节点使用
sigmoid
函数将它的输出限定在
0
和
1
之间，表示对观察值的预测分类的概率。夹在输入层和输出层之间的就是我们前
面说的“隐藏”层（其实它们根本没有隐藏）。这些隐藏层接连不断地将输入层的特征
值转换为一些值，这些值只要经输出层处理，就可以得到对应观察值的目标分类。有多 ...
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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.
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.