Problem 2 Write a naive implementation (i e non vectorized) of matrix multiplication, and then write an efficient implementation that utilizes Numpy's vectorization When writing the function ensure that the matrix dimensions are correct (print the message Wrong dimensions otherwise) def naive matmult(a, b) Multiply two matrices without vectorization YOUR CODE HERE raise NotImplementedError() a np random randn(3, 4) b np random randn(4, 5) assert almost equal(naive matmult(a, b) sum(), np dot(a, b) sum()) def vec matmult(a, b) Multiply two matrices with vectorization YOUR CODE HERE raise NotImplementedError() assert almost equal(vec matmult(a, b) sum(), np dot(a, b) sum())

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Problem 2 Write a naive implementation (i e  non vectorized) of matrix multiplication, and then write an efficient implementation that utilizes Numpy's vectorization  When writing the function ensure that the matrix dimensions are correct (print the message Wrong dimensions  otherwise)       def naive matmult(a, b)    Multiply two matrices without vectorization     YOUR CODE HERE raise NotImplementedError()      a   np  random  randn(3, 4) b   np random randn(4, 5) assert almost equal(naive matmult(a, b)  sum(), np dot(a, b)  sum())      def vec matmult(a, b)    Multiply two matrices with vectorization     YOUR CODE HERE raise NotImplementedError()      assert almost equal(vec matmult(a, b)  sum(), np  dot(a, b)  sum())

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Problem 2 Write a naive implementation (i.e. non-vectorized) of matrix multiplication, and then write an efficient implementation that utilizes Numpy's vectorization. When writing the function

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