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6章
与学习相关的技巧
本章将介绍神经网络的学习中的一些重要观点，主题涉及寻找最优权重
参数的最优化方法、权重参数的初始值、超参数的设定方法等。此外，为了
应对过拟合，本章还将介绍权值衰减、
Dropout
等正则化方法，并进行实现。
最后将对近年来众多研究中使用的
Batch Normalization
方法进行简单的介绍。
使用本章介绍的方法，可以高效地进行神经网络（深度学习）的学习，提高
识别精度。让我们一起往下看吧！
6.1
参数的更新
神经网络的学习的目的是找到使损失函数的值尽可能小的参数。这是寻
找最优参数的问题，解决这个问题的过程称为最优化（
optimization
）。遗憾的是，
神经网络的最优化问题非常难。这是因为参数空间非常复杂，无法轻易找到
最优解（无法使用那种通过解数学式一下子就求得最小值的方法）。而且，在
深度神经网络中，参数的数量非常庞大，导致最优化问题更加复杂。
在前几章中，为了找到最优参数，我们将参数的梯度（导数）作为了线索。
使用参数的梯度，沿梯度方向更新参数，并重复这个步骤多次，从而逐渐靠
近最优参数，这个过程称为随机梯度下降法（
stochastic gradient descent
），
简称
SGD
。
SGD
是一个简单的方法，不过比起胡乱地搜索参数空间，也算是“聪
明”的方法。但是，根据不同的问题，也存在比
SGD
更加聪明的方法。本节
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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.