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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8.5 深度学习的未来
261
Network
）这个技术的有趣之处。在这样的切磋中成长起来的
Generator
最终
会掌握画出足以以假乱真的图像的能力（或者说有这样的可能）。
之前我们见到的机器学习问题都是被称为监督学习（supervised
learning）的问题。这类问题就像手写数字识别一样，使用的是图像
数据和教师标签成对给出的数据集。不过这里讨论的问题，并没有
给出监督数据，只给了大量的图像（图像的集合），这样的问题称为
无监督学习（unsupervised learning）。无监督学习虽然是很早之前就
开始研究的领域（Deep Belief Network、Deep Boltzmann Machine
等很有名），但最近似乎并不是很活跃。今后，随着使用深度学习的
DCGAN等方法受到关注，无监督学习有望得到进一步发展。
8.5.3
自动驾驶
计算机代替人类驾驶汽车的自动驾驶技术有望得到实现。除了汽车制造
商之外，
IT
企业、大学、研究机构等也都在为实现自动驾驶而进行着激烈
的竞争。自动驾驶需要结合各种技术的力量来实现，比如决定行驶路线的路
线计划（
path plan
）技术、照相机或激光等传感技术等，在这些技术中，正
确识别周围环境的技术据说尤其重要。这是因为要正确识别时刻变化的环境、
自由来往的车辆和行人是非常困难的。
如果可以在各种环境中稳健地正确识别行驶区域的话，实现自动驾驶可
能也就没那么遥远了。最近，在识别周围环境的技术中，深度学习的力量备
受期待。比如，基于
CNN
的神经网络
SegNet
[42] ...
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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.