Hello.
Welcome back.
Now let's increase the number of inputs to three let's say we want to predict whether a person is sad
or happy given the temperature the humidity and the air quality.
This is what a neural network would look like.
Note that we have three weights one for temperature one for humidity the third one for air quality to
compute the predicted result we perform what is called the weighted sum.
This means we treat this neuro network as three separate single inputs single output neuro networks
and then we add up the predicted values of the three separate networks.
This is what the pseudocode would look like.
We multiply temperature inputs by weight we multiply humidity inputs by weight and then we multiply
air quality by weight.
But to be specific we multiply temperature inputs by weight one we more to apply humidity input by weight
to we more to apply air quality input by way 3 and then we add up the three products let's say we have
an array of temperature values and air quality values and humidity values if we want to perform that
prediction.
This is the pseudocode we're going to have.
So we have an array of three temperature a we have an array of temperatures an array of temperature
values sorry humidity values air quality values.
Let's say we just select the first one and then we create a new record input.
We take this new record input we pass it to our our multiple input and an end which is which means more
tuple input single output in your network just by passing this we should be able to make that prediction.
And this function is implemented.
We wrap the function around the multiple input and function the the uh the algorithm basically mathematically
is the weighted sum algorithm.
But to make it much more readable we wrap it around this other function that we call multiple input
over here multiple input underscore and so let's go and try to write some code to demonstrate this.
If you have any questions just let me know but it's as simple as this.