How do you calculate computational complexity of a neural network?

Suppose the input vectors have dimension n by 1. Then the time it takes to produce the output is a constant multiplication of n (when we use the big O). We keep in mind here that all we need is a two-layer neural network. So the time complexity is O(n) where n is the size of the input vector.

Do feedforward networks have backpropagation?

There is no pure backpropagation or pure feed-forward neural network.

What is feedforward and backpropagation in neural network?

Back propagation (BP) is a feed forward neural network and it propagates the error in backward direction to update the weights of hidden layers. The error is difference of actual output and target output computed on the basis of gradient descent method.

Do you need to read the essay on computational complexity?

This essay assumes familiarity with analytical complexity analysis of algorithms, and hereunder big-O notation. If you need a recap, you should read the essay on computational complexity before continuing. Looking at inference part of a feed forward neural network, we have forward propagation.

How to find the asymptotic complexity of the forward propagation procedure?

Looking at inference part of a feed forward neural network, we have forward propagation. Finding the asymptotic complexity of the forward propagation procedure can be done much like we how we found the run-time complexity of matrix multiplication. Before beginning, you should be familiar with the forward propagation procedure.

What is the feedforward propagation algorithm in neural network?

Feedforward propagation algorithm is as follows. First, to go from layer $i$to $j$, you do $$S_j = W_{ji}*Z_i$$ Then you apply the activation function $$Z_j = f(S_j)$$ If we have $N$layers (including input and output layer), this will run $N-1$times. Example

Is backtracking always exponential in complexity?

If you focus on the actual backtracking (or rather the branching possibilities at each step) you’ll only ever see exponential complexity. However, if there’s only so many possible states for the backtracking to explore, that’s all it can explore.