Consider a neural network with one hidden layer containing two nodes, input dimension 2 and output...

  

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Consider a neural network with one hidden layer containing two nodes, input dimension 2 and output dimension 1. That is, the fist layer contains two nodes to,1, 02, the hidden layer has two nodes ,1, 1,2, and the output layer one nodes 2,1. All nodes between consecutive layers are connected by an edge. The weights between node ,,, and +1 is denoted by w as (partially) indicated here: The nodes in the middle layer apply a differentiable activation function : RR, which has derivative o'. 0,1,1 IL X 6,22 1.1.1 1,1,2 (a) The network gets as input a 2-dimensional vector x = (1, 2). Give an expression for the output N(x) of the network as a function of , 2 and all the weights. Consider a neural network with one hidden layer containing two nodes, input dimension 2 and output dimension 1. That is, the fist layer contains two nodes to,1, 02, the hidden layer has two nodes ,1, 1,2, and the output layer one nodes 2,1. All nodes between consecutive layers are connected by an edge. The weights between node ,,, and +1 is denoted by w as (partially) indicated here: The nodes in the middle layer apply a differentiable activation function : RR, which has derivative o'. 0,1,1 IL X 6,22 1.1.1 1,1,2 (a) The network gets as input a 2-dimensional vector x = (1, 2). Give an expression for the output N(x) of the network as a function of , 2 and all the weights.