Finish the neural network cost with none-vectorized version.
Keep vectorizing the cost function.
Fully vectorized the cost function.

Note: you don't have to use the matrix multiplication.
You can use element-wise function to get the final result!

Whichever is eaiser to write/read, better in execution.
Add regularization to cost function.
add login info for 003 class
Finish the sigmoid gradient function.
Init the weight randomly.
Finish the un-regularized backpropagation!
Remove the debug message.

The debug messages are used to verify the matrix dimensions.
Correct the dimensions for a and Delta.

1. Delta should ALWAYS of the same dimension as Theta! That's the
definition.
2. a should have the bias term. So there should be logic to add the
bias term into vector a!.
Add regularization into gradient calculation.
Init check in for week6_ex5.
Finish the vectorized regularization cost function.
Add regularized gradient.
Finish the generation of training error and validation error.
Finish the feature mapping.
To calculate the final error, should REMOVE lambda.

This can be achieved by setting lambda=0 in the cost function!.