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 8章 深度学习
242
加深层的另一个好处就是使学习更加高效。与没有加深层的网络相比，
通过加深层，可以减少学习数据，从而高效地进行学习。为了直观地理解这
一点，大家可以回忆一下7.6 节的内容。7.6 节中介绍了
CNN
的卷积层会分
层次地提取信息。具体地说，在前面的卷积层中，神经元会对边缘等简单的
形状有响应，随着层的加深，开始对纹理、物体部件等更加复杂的东西有响应。
我们先牢记这个网络的分层结构，然后考虑一下 “狗”的识别问题。要
用浅层网络解决这个问题的话，卷积层需要一下子理解很多“狗”的特征。“狗”
有各种各样的种类，根据拍摄环境的不同，外观变化也很大。因此，要理解“狗”
的特征，需要大量富有差异性的学习数据，而这会导致学习需要花费很多时间。
不过，通过加深网络，就可以分层次地分解需要学习的问题。因此，各
层需要学习的问题就变成了更简单的问题。比如，最开始的层只要专注于学
习边缘就好，这样一来，只需用较少的学习数据就可以高效地进行学习。这
是为什么呢？因为和印有“狗”的照片相比，包含边缘的图像数量众多，并
且边缘的模式比“狗”的模式结构更简单。
通过加深层，可以分层次地传递信息，这一点也很重要。比如，因为提
取了边缘的层的下一层能够使用边缘的信息，所以应该能够高效地学习更加
高级的模式。也就是说，通过加深层，可以将各层要学习的问题分解成容易
解决的简单问题，从而可以进行高效的学习。
以上就是对加深层的重要性的増补性说明。不过，这里需要注意的是，
近几年的深层化是由大数据、计算能力等即便加深层也能正确地进行学习的 ...
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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.