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 4章 神经网络的学习
84
深 度 学 习 有 时 也 称 为 端 到 端 机 器 学 习（end-to-end machine
learning）。这里所说的端到端是指从一端到另一端的意思，也就是
从原始数据（输入）中获得目标结果（输出）的意思。
神经网络的优点是对所有的问题都可以用同样的流程来解决。比如，不
管要求解的问题是识别5，还是识别狗，抑或是识别人脸，神经网络都是通
过不断地学习所提供的数据，尝试发现待求解的问题的模式。也就是说，与
待处理的问题无关，神经网络可以将数据直接作为原始数据，进行“端对端”
的学习。
4.1.2
训练数据和测试数据
本章主要介绍神经网络的学习，不过在这之前，我们先来介绍一下机器
学习中有关数据处理的一些注意事项。
机器学习中，一般将数据分为训练数据和测试数据两部分来进行学习和
实验等。首先，使用训练数据进行学习，寻找最优的参数；然后，使用测试
数据评价训练得到的模型的实际能力。为什么需要将数据分为训练数据和测
试数据呢？因为我们追求的是模型的泛化能力。为了正确评价模型的泛化能
力，就必须划分训练数据和测试数据。另外，训练数据也可以称为监督数据。
泛化能力是指处理未被观察过的数据（不包含在训练数据中的数据）的
能力。获得泛化能力是机器学习的最终目标。比如，在识别手写数字的问题
中，泛化能力可能会被用在自动读取明信片的邮政编码的系统上。此时，手
写数字识别就必须具备较高的识别“某个人”写的字的能力。注意这里不是“特
定的某个人写的特定的文字”，而是“任意一个人写的任意文字
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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.