Welcome back.
Finally let's examine the neural network with a hidden layer.
The neural network with one hidden layer is this combination of the multiple inputs single output and
the single input multiple output neural network.
Essentially we take the output of one layer and feed it as input of another layer later on in the course.
You understand why our neural network will require hidden layers.
Let's see how the prediction of side can be derived.
We stuff from the inputs by taking the temperature input multiplying it by its correspondent weight
taken the humidity input multiplying it also by its corresponding weight and then taken the air quality
input multiplying it by its correspondent weight.
We then add the three products together.
This will give us the hidden zero.
We then repeat the same sequence to derive it in one and it into after we have derived hidden zero it
in one and hit in two.
We multiply it in zero in one and it in two by the corresponding weight and then we add up to three
product to get the prediction offside to compute all predictions.
We will have to perform two consecutive factor matrix multiplications.
We will have two sets of weight the first set will be the weight for the inputs to hit lay out computation
and the second set will be the weight for the hidden layer to output layer computation to compute our
neural network.
We first multiply our vector of input by the matrix of input to hidden weight.
This should result in a vector and like I mentioned earlier a vector is simply a one dimensional array
this vector show contain hidden zero hidden one on it and.
We take this vector and multiplied by the hit into output weight matrix this vector matrix multiplications
shall also result in effect for the vector shall contain the predictions for sad sake and active a simple
cluster.
Let's write some code to demonstrate what we've just broken ball.