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
深度学习是加深了层的深度神经网络。基于之前介绍的网络，只需通过
叠加层，就可以创建深度网络。本章我们将看一下深度学习的性质、课题和
可能性，然后对当前的深度学习进行概括性的说明。
8.1
加深网络
关于神经网络，我们已经学了很多东西，比如构成神经网络的各种层、
学习时的有效技巧、对图像特别有效的
CNN
、参数的最优化方法等，这些
都是深度学习中的重要技术。本节我们将这些已经学过的技术汇总起来，创
建一个深度网络，挑战
MNIST
数据集的手写数字识别。
8.1.1
向更深的网络出发
话不多说，这里我们来创建一个如图8
-
1 所示的网络结构的
CNN
（一 个
比之前的网络都深的网络）。这个网络参考了下一节要介绍的
VGG
。
如图8
-
1 所示，这个网络的层比之前实现的网络都更深。这里使用的卷
积层全都是3
×
3 的小型滤波器，特点是随着层的加深，通道数变大（卷积
层的通道数从前面的层开始按顺序以16、16、32、32、64、64 的方式增加）。
此外，如图 8
-
1 所示，插入了池化层，以逐渐减小中间数据的空间大小；并且，
后面的全连接层中使用了
Dropout
层。
第 8章 深度学习
236
这个网络使用
He
初始值作为权重的初始值，使用
Adam
更新权重参数。
把上述内容总结起来，这个网络有如下特点。
•
基于 3×3
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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.