 <p>There is no point in representing it as a mathematical function because it won't save you any computations.</p> <p>Indeed all you need is the weights, biases, activation and your architecture. I'm assuming it is a simple feedforward network as you said, you need to implement some kind of matrix multiplication and addition in C. Also, you'll need to implement the activation function. After that, you're ready to go. Your feed forward NN is ready to be implemented. If the C code will not be used for training, it won't be necessary to implement the backpropagation algorithm in C.</p> <p>A feedforward layer would be implemented as follows:</p> <p><strong>Output = Activation_function(Input * weights + bias)</strong></p> <p>Where,</p> <p><strong>Input</strong>: (1 x number_of_input_parameters_for_this_layer)</p> <p><strong>Weights</strong>: (number_of_input_parameters_for_this_layer x number_of_neurons_for_this_layer)</p> <p><strong>Bias</strong>: (1 x number_of_neurons_for_this_layer)</p> <p><strong>Output</strong>: (1 x number_of_neurons_for_this_layer)</p> <p>The output of a layer is the input to the next layer.</p>