Let's practice taking gradients, which is a key operation for being...

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Let's practice taking gradients, which is a key operation for being able to optimize continuous functions. For W E Rd (represented as a column vector) and constants a, b E Rd (also represented as column vectors) and LER, define the scalar-valued function where the vector is W = (w1, wa)T and as the L2 norm. Compute the gradient Vf(w). Recall: the gradient is a d-dimensional vector of the partial derivatives with respect to each Wi: If you're not comfortable with vector calculus, first warm up by working out this problem using scalars in place of vectors and derivatives in place of gradients. Not everything for scalars goes through for vectors, but the two should at least be consistent with each other (when d = 1). Do not write out summation over dimensions, because that gets tedious.

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