线性与非线性神经网络? [英] Linear vs nonlinear neural network?

问题描述

我是机器学习和神经网络的新手.我知道如何建立非线性分类模型，但是我目前的问题是连续不断的输出.我一直在寻找有关神经网络回归的信息，但我遇到的只是有关 linear 回归的信息-与 nonlinear 案例无关.这很奇怪，因为为什么有人还是要使用神经网络来解决简单的线性回归?这不是像用核弹杀死苍蝇吗?

I'm new to machine learning and neural networks. I know how to build a nonlinear classification model, but my current problem has a continuous output. I've been searching for information on neural network regression, but all I encounter is information on linear regression - nothing about nonlinear cases. Which is odd, because why would someone use neural networks to solve a simple linear regression anyway? Isn't that like killing a fly with a nuclear bomb?

所以我的问题是:是什么使神经网络非线性? (隐藏层?非线性激活函数?)还是我对线性"一词有完全错误的理解-线性回归NN能否准确地建模比y = aX + b更复杂的数据集? 线性"一词是否与物流"相反?

So my question is this: what makes a neural network nonlinear? (Hidden layers? Nonlinear activation function?) Or do I have a completely wrong understanding of the word "linear" - can a linear regression NN accurately model datasets that are more complex than y=aX+b? Is the word "linear" used just as the opposite of "logistic"?

(我打算使用TensorFlow，但TensorFlow线性模型教程以二进制分类问题为例，因此也无济于事.)

(I'm planning to use TensorFlow, but the TensorFlow Linear Model Tutorial uses a binary classification problem as an example, so that doesn't help me either.)

推荐答案

对于初学者来说，神经网络可以对任何函数(不仅是线性函数)建模，请看一下-

For starters, a neural network can model any function (not just linear functions) Have a look at this - http://neuralnetworksanddeeplearning.com/chap4.html.

神经网络具有非线性激活层，这使神经网络具有非线性元素.

A Neural Network has got non linear activation layers which is what gives the Neural Network a non linear element.

用于关联输入和输出的功能由神经网络及其获得的训练量决定.如果您提供两个具有线性关系的变量，那么只要您不过度拟合，网络就会学到这一点.同样，足够复杂的神经网络可以学习任何功能.

The function for relating the input and the output is decided by the neural network and the amount of training it gets. If you supply two variables having a linear relationship, then your network will learn this as long as you don't overfit. Similarly, a complex enough neural network can learn any function.

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