…hout explicitly calculating the gradient

…ew cost function on stan

…everal changes to styling and getting rid of vanilla loops + code restructure

…e aliases, actual unit tests haven't changed

…stOrderSAGCostFunction

…ers, since they should be immutable

…ake sure input is correct

…the Minimizers and updaters

…t uses stan

…ction, no major changes here [ci skip]

…on, including removing pointers [ci skip]

…ochasticCostFunction, and made get_gradient() thread safe. [ci skip]

…a neural network layer [ci skip]

…n from logic of layer

…neural network, the logistic/softmax/relu layers will inherit from it [ci skip]

…t aliased instead of A

…lled StanNeuralLogisticLayer and computes activations as required [ci skip]

… MVP

layers

The class is composed of parameters, and is only responsible for forward
propagation of the layers.

The Network will be used to generate a StanMatrix of outputs, which will
then be used with Minimizers and StanCostFunctions to minimize the
neural network's parameters

[ci skip]

…nd initialize_parameters to pass indices of start and end indices of the stan vector of parameters

…orairly and replace it with indices for stan vector

…ation, but there is still some syntax errors that are being fixed, once they are addressed, we can start the testing

…tax errors apart from compute_activations(input) which has been silenced for now, needs discussion before fixing; 3) Got rid of get_section() logic and replaced it with other logic that doesn't have to copy the vector; 4) Got rid of get_larger_activation logic since it wasn't needed ; 5) Fixed all signature errors of compute_activations(params, i, j, layers) and its specializations; 6) Got rid of some typos, still many typos that need addressing TODO

… done, the next part is to test it to check implementation details