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
Answer:
Iteration
Explanation:
The insertion sort is based on repetition of comparing one data array (or element in a list) with the others at its left to reorganize it, normally following a size criteria (from small to big or the other way around).
At each iteration, the algorithm takes one element and compares it one by one to the others until it fit the specified criteria. Later on, it creates a space, moving the other elements, to insert it. Later, it goes to the next element and the iteration repeats all the way through. It has some advantages over other sorting algorithms because it is easy to deploy and program it in many different languages, but at the same time it can be terribly slow when sorting large amount of data.
Answer:
um ahh <em> </em><em>badl</em><em>y</em><em> </em>
Explanation:
i really don't know the answer
