-
The last pieces in neural networks to be implemented
-
Init all theta to zero may eventually correct in logistic regression, but fail in neural networks
-
the result will be looping forever as the result(hidden units) is exactly is the same as input
-
The problem will be solved by random initialization
-
the random will be the value between 0 and 1
-
the epsilon is init that we manually set, different to what we know about epsilon in gradient checking
-
In summary, random init value close to zero
-
do backprop, gradient checking, do advanced animation , to minimize cost function j(theta) using random init with symmetry breaking
-
This will find a good random value of theta
