Added some files

README

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+Common useful functions

computeNumericalGradient.m

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+function numgrad = computeNumericalGradient(J, theta)
+%COMPUTENUMERICALGRADIENT Computes the gradient using "finite differences"
+%and gives us a numerical estimate of the gradient.
+%   numgrad = COMPUTENUMERICALGRADIENT(J, theta) computes the numerical
+%   gradient of the function J around theta. Calling y = J(theta) should
+%   return the function value at theta.
+
+% Notes: The following code implements numerical gradient checking, and 
+%        returns the numerical gradient.It sets numgrad(i) to (a numerical 
+%        approximation of) the partial derivative of J with respect to the 
+%        i-th input argument, evaluated at theta. (i.e., numgrad(i) should 
+%        be the (approximately) the partial derivative of J with respect 
+%        to theta(i).)
+%                
+
+numgrad = zeros(size(theta));
+perturb = zeros(size(theta));
+e = 1e-4;
+for p = 1:numel(theta)
+    % Set perturbation vector
+    perturb(p) = e;
+    loss1 = J(theta - perturb);
+    loss2 = J(theta + perturb);
+    % Compute Numerical Gradient
+    numgrad(p) = (loss2 - loss1) / (2*e);
+    perturb(p) = 0;
+end
+
+end

l2row.m

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+function [y] = l2row(x)
+    normeps = 1e-8;
+    epssumsq = sum(x.^2, 2) + normeps;
+    l2rows = sqrt(epssumsq);
+    y = bsxfun(@rdivide, x, l2rows);
+end

l2rowg.m

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+function [grad] = l2rowg(x, y, outderv)
+%L2ROWG Assumes examples in columns
+
+    normeps = 1e-8;
+    if (~exist('outderv','var')||isempty(outderv))
+        error('Requires outderv of previous layer to compute gradient!');
+    end
+
+    epssumsq = sum(x.^2,2) + normeps;	
+
+    l2rows = sqrt(epssumsq);
+
+    if (~exist('y','var')||isempty(y))
+         y = bsxfun(@rdivide,x,l2rows);
+    end
+
+    grad = bsxfun(@rdivide, outderv, l2rows) - ...
+        bsxfun(@times, y, sum(outderv.*x, 2) ./ epssumsq);
+end