APPLICATION OF SUPERVISED LEARNING
NEURAL NETWORK
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
a way to determine how well the machine learning model has performed given the different values of each parameter
|
|
|
an optimization algorithm used to find the values of parameters (coefficients) of a function (f) that minimizes a cost function (cost)
|
|
|
function describes how computationally expensive is a neural network
|
|
|
function used to enable Neural Network to solve non-linear problems
|
Detailed explanation-1: -The tanh function is just another possible function that can be used as a non-linear activation function between layers of a neural network.
Detailed explanation-2: -The main job of an activation function is to introduce non-linearity in a neural network. A neural network is modelled after the human brain that consists of neurons. To obtain the output, a neural network accepts an input and weights summed with bias before arriving at the output.
Detailed explanation-3: -Sigmoid Function and Vanishing Gradient The sigmoid activation function is a popular choice for the nonlinear activation function for neural networks.
Detailed explanation-4: -What is a Neural Network Activation Function? An Activation Function decides whether a neuron should be activated or not. This means that it will decide whether the neuron’s input to the network is important or not in the process of prediction using simpler mathematical operations.
Detailed explanation-5: -Tanh. The tanh function is best suited for the classification of two different classes. Like sigmoid, it’s nonlinear and also forms an S-shaped graph.