# Random Initialization

- 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