APPLICATION OF SUPERVISED LEARNING
NEURAL NETWORK
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
This is where we insert the initial data for the neural network.
|
|
Input
|
|
|
Hidden Layer
|
|
|
Output
|
|
|
None of the above
|
Explanation:
Detailed explanation-1: -Input Layer– First is the input layer. This layer will accept the data and pass it to the rest of the network.
Detailed explanation-2: -Weight initialization is a procedure to set the weights of a neural network to small random values that define the starting point for the optimization (learning or training) of the neural network model.
Detailed explanation-3: -One of the first steps in building a neural network is finding the appropriate activation function. In our case, we wish to predict if a picture has a cat or not. Therefore, this can be framed as a binary classification problem. Ideally, we would have a function that outputs 1 for a cat picture, and 0 otherwise.
There is 1 question to complete.