Answer:
Yep, saw her posts. Thanks for the warning though. I reported one of her posts. It's just another example of how desperate people are and what measures they will take to get attention. She'll probably be banned soon
Explanation:
Thank you for the warning though. I hope this reaches more people.
Answer:
40
Explanation:
Given that:
A neural network with 11 input variables possess;
one hidden layer with three hidden units; &
one output variable
For every input, a variable must go to every node.
Thus, we can calculate the weights of weight with respect to connections to input and hidden layer by using the formula:
= ( inputs + bias) × numbers of nodes
= (11 + 1 ) × 3
= 12 × 3
= 36 weights
Also, For one hidden layer (with 3 nodes) and one output
The entry result for every hidden node will go directly to the output
These results will have weights associated with them before computed in the output node.
Thus; using the formula
= (numbers of nodes + bais) output, we get;
= ( 3+ 1 ) × 1
= 4 weights
weights with respect to input and hidden layer total = 36
weights with respect to hidden and output layer total = 4
Finally, the sum of both weights is = 36 + 4
= 40
Boolean operators it is
all the best
Answer:
Explanation:
The following code is written in Java and is a function/method that takes in an int array as a parameter. The type of array can be changed. The function then creates a counter and loops through each element in the array comparing each one, whenever one element is found to be a duplicate it increases the counter by 1 and moves on to the next element in the array. Finally, it prints out the number of duplicates.
public static int countDuplicate (int[] arr) {
int count = 0;
for(int i = 0; i < arr.length; i++) {
for(int j = i + 1; j < arr.length; j++) {
if(arr[i] == arr[j])
count++;
}
}
return count;
}