Answers.Body: <blockquote>
    <p>In Single Perceptron / Multi-layer Perceptron(MLP), we only have linear separability because they are composed of input and output layers(some hidden layers in MLP)</p>
  </blockquote>
  
  <p>This is <strong>wrong</strong>. </p>
  
  <p>A multi-layer perceptron (i.e. a feed-forward neural network) with non-linear activation functions can perform non-linear classification and regression. In fact, an MLP with one hidden layer with an arbitrary number of hidden nodes, each of them with a sigmoid (which is a non-linear function), can approximate any continuous function (up to an approximation error).</p>
  
  <p>On the other hand, perceptrons can't do that. They perform only linear classification/regression.</p>
  
  <blockquote>
    <p>I thought the MLP was a non-linear classifier.</p>
  </blockquote>
  
  <p>You're right, unless the MLP only uses linear activation functions. In that case, it won't be able to perform non-linear classification/regression.</p>
  
  <p>(P.S.: I suggest you always question the truth and correctness of what you read on the web, especially, on sites like Medium, as you actually did!)</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-23T10:45:16.527Z
Answers.Text: Is it true to say that, in a MLP the neurons in the hidden layer can use only nonlinear activation functions while the neurons in the output layer can use or a linear or a non linear activation function ?
Answers.UserDisplayName: AleWolf
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-23T11:18:19.077Z
Answers.Text: @AleWolf They can use linear or non-linear activation functions everywhere, in principle.
Answers.UserDisplayName: nbro
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-23T11:49:30.013Z
Answers.Text: But, if they use linear activation function everywhere then that particular MLP would be linear, right ?
Answers.UserDisplayName: AleWolf
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-23T11:56:19.333Z
Answers.Text: @AleWolf It would be linear in the sense that it would represent a straight-line.
Answers.UserDisplayName: nbro
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-22T18:55:13.377Z
Answers.LastActivityDate: 2020-04-22T19:01:12.027Z
Answers.LastEditDate: 2020-04-22T19:01:12.027Z
Answers.UserDisplayName: nbro
Body: <p>In this <a href="https://towardsdatascience.com/radial-basis-functions-neural-networks-all-we-need-to-know-9a88cc053448" rel="nofollow noreferrer">page</a> it's told:</p>
  
  <blockquote>
    <p>In Single Perceptron / Multi-layer Perceptron(MLP), we only have linear separability because they are composed of input and output layers(some hidden layers in MLP)</p>
  </blockquote>
  
  <p>What does it mean? I thought the MLP was a non-linear classifier. Could you explain it to me?</p>
  
Tags: <deep-learning><multilayer-perceptrons><perceptron>
Title: Why can't MLPs perform non-linear regression and classification?
UserDisplayName: AleWolf
ThreadText: # Why can't MLPs perform non-linear regression and classification?
  
  (Asked by: username_0 on 2020-04-22)
  
  In this [page](https://towardsdatascience.com/radial-basis-functions-neural-networks-all-we-need-to-know-9a88cc053448) it's told:
  
  > In Single Perceptron / Multi-layer Perceptron(MLP), we only have linear separability because they are composed of input and output layers(some hidden layers in MLP)
  
  What does it mean? I thought the MLP was a non-linear classifier. Could you explain it to me?
  
  **Tags:** deep-learning, multilayer-perceptrons, perceptron
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-04-22. Score: 2 | Accepted answer
  
  > In Single Perceptron / Multi-layer Perceptron(MLP), we only have linear separability because they are composed of input and output layers(some hidden layers in MLP)
  
  This is **wrong**.
  
  A multi-layer perceptron (i.e. a feed-forward neural network) with non-linear activation functions can perform non-linear classification and regression. In fact, an MLP with one hidden layer with an arbitrary number of hidden nodes, each of them with a sigmoid (which is a non-linear function), can approximate any continuous function (up to an approximation error).
  
  On the other hand, perceptrons can't do that. They perform only linear classification/regression.
  
  > I thought the MLP was a non-linear classifier.
  
  You're right, unless the MLP only uses linear activation functions. In that case, it won't be able to perform non-linear classification/regression.
  
  (P.S.: I suggest you always question the truth and correctness of what you read on the web, especially, on sites like Medium, as you actually did!)
  
  > **Comments:**
  > **username_0**: Is it true to say that, in a MLP the neurons in the hidden layer can use only nonlinear activation functions while the neurons in the output layer can use or a linear or a non linear activation function ?  
  > **username_1**: @username_0 They can use linear or non-linear activation functions everywhere, in principle.  
  > **username_0**: But, if they use linear activation function everywhere then that particular MLP would be linear, right ?  
  > **username_1**: @username_0 It would be linear in the sense that it would represent a straight-line.  
  
  ---
  
  