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
A common and extremely useful feature of most online dictionaries is <em />interoperable browser.
Answer:
Transducer is a type of device that converts one type of signal into another.
Answer:
Assuming this is Python, I would do something like the following:
Explanation:
hourWage= float(input ("What is your hourly wage?: "))
regularHours= float(input ("How many regular hours did you work this week?: "))
overtimeHours= float(input ("How many overtime hours did you have this week?: "))
overtimeWage= (1.5*hourWage)
totalWeeklyPay= (hourWage*regularHours)+(overtimeHours*overtimeWage)
print= ("Your total weekly pay is: " ,totalWeeklyPay)
I hope this works!
