Hello.
Welcome back.
This is our neural network again.
The green text here indicates the lower numbers and the orange text indicates the number of units in
each layer to compute the z value of Layer 1 we compute the superscript 1 e course W superscript 1 x
plus b superscript 1.
Just like we can see over here X is a column vector.
Therefore its shape is three by one because w superscript 1 which is the weight for Layer 1 has 5 units
and each unit is receiving 3 input then its shape shall be 5 by 3 superscript 1 is the bias for Layer
1 which has 5 unit the shape of B superscript 1 choppy 5 by 1.
If we solve this equation with these input shapes the resultant shape which is the shape of C superscript
1 shall be 5 by 1.
We can actually generalize the shapes of various matrices of a 1 neural network.
Let's see to find capitals of layer L which shall contain the zeal of all training examples.
We compute capital W layer l capital x plus b like we have in this equation over here D show of capital
X will be the number of features which we denote us and superscript 0 by the number of training examples
which we denotes by M the shape of capital W superscript l shall be the number of units of layer L by
the number of features or input the shape of superscript l shall be the number of units of layer L by
the number of examples and the shape of capital Z superscript l shall be the number of units of layer
l by training examples.
In summary the shape of c of a single training example of layer l written here a small c superscript
l is equal to the number of units of layer l by 1 the shape of the vector rise version of c which is
written as capital C is equal to the number of units of layer L by the number of training examples which
is denoted here as m the shape of W superscript l is n superscript l by N superscript l minus 1 the
shape of P superscript l is n superscript l by one the shape of D W superscript l is the same as the
shape of W superscript l the shape of d be superscript l is the same as the shape of P superscript l
let s conclude this lecture by talking a bit about parameters and hyper parameters parameters of a one
new note work out the weight and bias is denoted here as W and b the hyper parameters are the parameters
we can change in order to improve the performance of our new network.
These include the landing rate the number of iterations number of hidden layers number of hitting unit
and activation functions.
So this all the risk for this lesson and I shall see you in the next lesson.