APPLICATION OF SUPERVISED LEARNING
NEURAL NETWORK
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
collection of ‘neurons’ operating together at a specific depth within a neural network
|
|
Bias
|
|
|
Weight
|
|
|
Neuron
|
|
|
Layers
|
Explanation:
Detailed explanation-1: -Hidden Layer(s): All of the input variables are combined across one or more nodes in the hidden layer, This essentially creates new features, derived from the inputs provided. Typically all input nodes are connected to all nodes in the hidden layer.
Detailed explanation-2: -The first layer in a network is called the input layer, while the last is called an output layer. All the layers between input and output are referred to as hidden layers. Each layer is typically a simple, uniform algorithm containing one kind of activation function.
There is 1 question to complete.