#010A Gradient Descent for Neural Networks
In this post we will see how to implement gradient descent for one hidden layer Neural Network as presented in the picture below. One hidden layer Neural Network Parameters for one hidden layer Neural Network are \(\textbf{W}^{[1]} \), \(b^{[1]} \), \(\textbf{W}^{[2]} \) and \(b^{[2]} \). Number of unitis in each layer are: input of a Neural Network is feature vector ,so the length of “zero” layer \(a^{[0]} \) is the size of an input feature…
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