Contents
What makes a neural network non linear?
A Neural Network has got non linear activation layers which is what gives the Neural Network a non linear element. The function for relating the input and the output is decided by the neural network and the amount of training it gets.
What is non-linear activation function?
Non-Linear Activation Functions Non-linear functions address the problems of a linear activation function: They allow backpropagation because they have a derivative function which is related to the inputs. They allow “stacking” of multiple layers of neurons to create a deep neural network.
Why is the decision boundary of a neural network complex?
We observe that, as we increase the number of neurons, the model is able to classify the points more accurately. The decision boundary is complex due to it being a non linear combination (via activation functions) of individual decision boundaries.
Why do we need a non linear decision boundary?
Non-linearity allows for more complex decision boundaries. One potential decision boundary for our XOR data could look like this. We know that the imitating the XOR function would require a non-linear decision boundary. But why do we have to stick with a single decision boundary?
How are neural networks used to solve nonlinear problems?
Following three figures depict a single neuron based neural trying to solve the problem. We observe that a single neuron based neural net is, as expected, giving a linear decision boundary which irrespective of the configuration (activation function, learning rate etc) is not able to solve a nonlinear problem.
How are neural networks used to classify data?
On an abstract level, it can be viewed as multiple classifiers combining in a nonlinear manner to fetch the nonlinear decision plane. It can be concluded that when data is non linear, a layer of multiple neurons with non linear activation function can classify it.