人工神经网络初探

什么是人工神经网络?本文将从3个方面阐述。概念,直观例子,应用。

1.概念

人工神经网络(Artificial Neural Network,ANN),简称神经网络(Neural Network,NN)或类神经网络,是一种模仿生物神经网络的结构和功能的数学模型。它由大量神经元联结进行运算,大多数情况下人工神经网络能够在外界信息的基础上改变内部结构,是一种自适应系统,通俗的讲就是具备学习功能。现代神经网络是一种非线性统计性数据建模工具。

神经网络结构:

1.输入层:众多神经元(Neuron)接受大量非线形输入讯息。输入的讯息称为输入向量,是原始输入数据。

2.隐藏层:主要结构,不同神经网络主要在于隐藏层参数调整,每层神经元对于给定输入数据转换成输出

3.输出层:输出结果

2.直观例子

斯坦福大学的神经网络公开课中有个简单的关于预测房子价值的例子。

《人工神经网络初探》

如图所示,输入关于房子数据,房子大小,卧室个数,邮政编码,富裕程度4个参数。第一层神经元得到同样的参数,但是输出是不一样的,比如最上面的神经元映射是家庭成员个数,那么它跟邮政编码,富裕程度关系相对于大小和卧室个数来说就要小一点,相对的,X1,X2权重就要大,X3,X4权重就要小,这样第一层神经网络的输出就是各不相同的。

第一层的输出作为第二层的输入,第二层神经元的输入有3个参数,通过一个转换函数得到Y,而在这个过程中,详细的每个输入的权重,偏移量等都是通过学习自动适应的,我们要做的就是设定神经网络层数,神经元个数,以及转换函数,然后给与足够多的数据量,神经网络就能够自动学习并预测出给定条件的房子价格。

3.应用

神经网络基本用于监督学习中,下面这张表详细描述了相关应用实例。

输入 输出 应用 神经网络类型
房子特征 价格 不动产预测 标准神经网络(standard NN)
广告,用户 是否点击 广告推荐 标准神经网络(Standard NN)
图片 类别 图片识别 卷积神经网络(CNN)
英文 中文 翻译 循环神经网络(RNN)
图片,雷达 其他车位置 自动驾驶 综合神经网络(hybrid NN)

各种神经网络图片表示方式

《人工神经网络初探》

总结

本文是个人工神经网络的初步认识,了解神经网络的构成,以及各种使用的应用场景。

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