How to understand the weights and biases for beginners?
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.