July 2018
Intermediate to advanced
310 pages
8h 21m
Chinese
Content preview from 深度学习入门 : 基于Python的理论与实现
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第5章
误差反向传播法
上一章中，我们介绍了神经网络的学习，并通过数值微分计算了神经网
络的权重参数的梯度（严格来说，是损失函数关于权重参数的梯度）。数值微
分虽然简单，也容易实现，但缺点是计算上比较费时间。本章我们将学习一
个能够高效计算权重参数的梯度的方法——误差反向传播法。
要正确理解误差反向传播法，我个人认为有两种方法：一种是基于数学式；
另一种是基于计算图（
computational graph
）。前者是比较常见的方法，机器
学习相关的图书中多数都是以数学式为中心展开论述的。因为这种方法严密
且简洁，所以确实非常合理，但如果一上来就围绕数学式进行探讨，会忽略
一些根本的东西，止步于式子的罗列。因此，本章希望大家通过计算图，直
观地理解误差反向传播法。然后，再结合实际的代码加深理解，相信大家一
定会有种“原来如此！”的感觉。
此外，通过计算图来理解误差反向传播法这个想法，参考了
Andrej
Karpathy
的博客
[4]
和他与
Fei-Fei Li
教授负责的斯坦福大学的深度学习课程
CS231n
[5]
。
5.1
计算图
计算图将计算过程用图形表示出来。这里说的图形是数据结构图，通
过多个节点和边表示（连接节点的直线称为“边”）。为了让大家熟悉计算图，
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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.