The following equation captures the computation in a ResNet block. What goes into the two blanks above? a[l+2]=g(W[l+2]g(W[l+1]a[l]+b[l+1])+bl+2+_______ )+_______

The two blanks in the ResNet block equation should be filled as follows:

a[l+2] = g(W[l+2]g(W[l+1]a[l] + b[l+1]) + b[l+2] + _______ ) + _______

The first blank should be filled with "a[l]", which represents the identity shortcut connection that skips one or more layers. The second blank should be left empty, as there is no additional term added after the activation function "g".

So, the completed equation should look like this:

a[l+2] = g(W[l+2]g(W[l+1]a[l] + b[l+1]) + b[l+2] + a[l] )

This equation represents the operation of a ResNet block, where "a" is the activation, "W" is the weight, "b" is the bias, "g" is the activation function, and "l" is the layer index. The term "a[l]" is added to the output of the block to form a shortcut connection, which helps to mitigate the problem of vanishing gradients in deep neural networks.