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Python机器学习手册:从数据预处理到深度学习
book

Python机器学习手册:从数据预处理到深度学习

by Chris Albon
July 2019
Intermediate to advanced
365 pages
8h 13m
Chinese
Publishing House of Electronics Industry
Content preview from Python机器学习手册:从数据预处理到深度学习
302
20
神经网络
的小随机值。一旦一个观察值(更常见的情况是一组观察值的集合,又被称为
批次
<
batch
>
)被传给神经网络,它的输出值就会被拿来用损失函数与观察值的真实值进行比
较。这个过程叫作
前向传播
forward propagation
)。接下来,算法“向后”在神经网络
中传播,它识别出每个参数对预测值和真实值之间的差异的影响程度,这个过程叫作
向传播
backward propagation
)。对于每一个参数,优化算法决定应该如何调整权重值
才能改善输出值。
神经网络通过对训练集中的每个观察值重复做多次前向传播和反向传播(所有的观察值
都通过网络传递一次就被称为一个
epoch
,这种训练一般都需要多个
epoch
),以此迭代
更新参数的值。
在本章中,我们将使用流行的
Python
Keras
来创建、训练和评估几种神经网络。
Keras
是一个高层的软件包,使用了像
TensorFlow
Theano
这样的库来作为它的“引擎”。
对于我们来说,
Keras
的优势就在于它可以使我们专注于神经网络的设计和训练,把张
量运算的细节留给其他库来完成。
使用
Keras
创建的神经网络既可以用
CPU
(比如,你的笔记本电脑)训练,也可以用
GPU
(比
如,某些专门为深度学习而设计的计算机)训练。在现实世界中,我们强烈建议使用
GPU
来训练神经网络 ;但是为了便于学习,本书中所有的神经网络都很小而且很简单,
用笔记本电脑也可以在几分钟之内把它们训练出来。你只需要记住,对于大型神经网络
和大量训练数据,使用
CPU
来训练会比使用 ...
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Publisher Resources

ISBN: 9787121369629