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diff --git a/NN/mlclass-ex4/mlclass-ex4/randInitializeWeights.m b/NN/mlclass-ex4/mlclass-ex4/randInitializeWeights.m
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+function W = randInitializeWeights(L_in, L_out)
+%RANDINITIALIZEWEIGHTS Randomly initialize the weights of a layer with L_in
+%incoming connections and L_out outgoing connections
+%   W = RANDINITIALIZEWEIGHTS(L_in, L_out) randomly initializes the weights
+%   of a layer with L_in incoming connections and L_out outgoing
+%   connections.
+%
+%   Note that W should be set to a matrix of size(L_out, 1 + L_in) as
+%   the first row of W handles the "bias" terms
+%
+
+% You need to return the following variables correctly
+W = zeros(L_out, 1 + L_in);
+
+% ====================== YOUR CODE HERE ======================
+% Instructions: Initialize W randomly so that we break the symmetry while
+%               training the neural network.
+%
+% Note: The first row of W corresponds to the parameters for the bias units
+%
+
+eps = 0.12;
+
+W = rand(L_out, 1 + L_in) * 2 * eps - eps;
+
+
+
+
+% =========================================================================
+
+end