权重和偏差在神经网络中起什么作用？

本文主要是介绍权重和偏差在神经网络中起什么作用？，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

介绍 (Introduction)

We all know that an Artificial Neuron is a basic building block of the neural network. Before we get into the topic, “what is the role of weights and bias in a Neural Network “, let us understand the skeleton of this Artificial Neuron.

我们都知道，一个人工神经是神经网络的基本构建块。在进入主题“神经网络中的权重和偏见的作用”之前，让我们了解这种人工神经元的骨架。

基本的人工神经元的组成： (Components of the basic Artificial Neuron:)

Inputs: Inputs are the set of values for which we need to predict the output value. They can be viewed as features or attributes in a dataset.
输入：输入是我们需要为其预测输出值的一组值。可以将它们视为数据集中的要素或属性。
Weights: weights are the real values that are associated with each feature which tells the importance of that feature in predicting the final value. (we will know more about in this article)
权重：权重是与每个要素关联的实际值，表明了该要素在预测最终值中的重要性。 (我们将在本文中了解更多信息)
Bias: Bias is used for shifting the activation function towards left or right, it can be referred to as a y-intercept in the line equation. (we will know more about this in this article)
偏差：偏差用于将激活函数向左或向右移动，在线性方程式中可以称为y截距。 (我们将在本文中对此有更多了解)
Summation Function: The work of the summation function is to bind the weights and inputs together and find their sum.
求和函数：求和函数的作用是将权重和输入绑定在一起，以求和。
Activation Function: It is used to introduce non-linearity in the model.
激活函数：用于在模型中引入非线性。

如果没有重量怎么办？ (What if there are no weights?)

We will come to know one’s importance only during its absence

只有在缺席的情况下，我们才会知道自己的重要性

As the statement speaks, let us see what if there are no weights involved in a neuron, for simplicity let us consider there are only two features in the dataset, ie input vector X ϵ [ x₁ x₂ ], and our model task it to perform binary classification.

正如该声明所言，让我们看看如果神经元中没有权重，为简单起见，让我们考虑数据集中只有两个特征，即输入向量X ϵ [x₁x²]，以及我们要执行的模型任务二进制分类。