Y = Wx + b
Here, I am assuming that the h
is in subscript :
Y
is the value that we want to predict
x
is the input
b
is the bias
W
is the weight
Now, the question that you asked If I understood clearly :
You want to know, How can you get the weight and Bias of your model ? And are the W input to your machine learning model or is it the neural networks task to find the weight for you ?
Weight W
is the coefficient of the input x
which when combined with bias b
returns the predicted value Y
. Note that weight W
is the coefficient of the feature input x
.
The sole aim to run a machine / deep learning algorithm is to find the best set of weights corresponding to each feature and the bias. The bias does not correspond to every weight w
, It corresponds to each layer in the network.