Add nnCostFunction.m

neural network cost function
change comment of variable m
Add bias term to X which corresponds to the first column of 1's
Compute activation term a^(2) which is given from input layer
Add bias term to the input of hidden layer in which a column of 1's is inserted in front
Compute h_theta which is given from hidden layer
Initialise the rolled version of y
Map the unrolled version of y to rolled one
Using element-wise multiplication to compute non-regularized J

Using unrolled yMapped and h_theta to compute J takes 0.006541 seconds,
while using element-wise multiplication to compute J takes 0.0033979
seconds, which is faster twice than unrolled mathod.
Delete white space and the comments being recorded in GitHub
remove bias terms in Theta1 and Theta2
Regularize cost function
Add comment to yMapped
Add sigmoidGradient.m
Compute gradient of sigmoid function

Let g(z) be sigmoid function of z.  Then g'(z) = g(z)*(1 - g(z)).
Add randInitializeWeights.m

Random initializer of the weights Theta
Set upper bound for random number

Algorithm is given by the footnote in p7 of ex4.pdf
Delete white space
Ramdomise the weights for Theta in the range of +- epsilon_init
Change variable names of theta 1 and 2 which are used in computation of cost function