# How to tell how many learnable parameters there are in the neural network?

Given that $$x{(i)} \in R^{100}$$. And the fully-connected layer $$f(.)$$ is $$f(x^{(i)}) = \sigma(Wx^{(i)})$$ where W is a 1000 $$\times$$ 100 weight matrix and $$\sigma(.)$$ is a point-wise nonlinearity.

I was looking at this question: Number of parameters in an artificial neural network for AIC

For this specific examples, there is an input layer, one hidden layer, and one output layer? How can I compute the number of learnable parameters here? The input dimension is $$(100 \times 1)$$ and the output dimension is $$(1000 \times 1)$$ I believe.

Is the numbber of learning parameters just $$(100 \times 1000) + (1000 \times 1) = 101000$$? Or am i misunderstanding?