I was following andrew ng machine learning course. I didn't understand something in the part where he was trying to formulate the optimization problem for svm. Specifically, How can you get formulation 2 from formulation 1. Formulation 1: $$\max_{\gamma, w, b}\gamma$$ subject to: $$y^{(i)}(w^Tx^{(i)}+b)\geq\gamma, i=1,\dots m,\\||w||=1$$
Formulation 2: $$\max_{\hat\gamma, w, b}\dfrac{\hat\gamma}{||w||}$$ subject to: $$y^{(i)}(w^Tx^{(i)}+b)\geq\hat\gamma, i=1,\dots m$$