Vectorization and looping in python
Vectorised and non vectorised code comparison vectorization is required in python when we are dealing with matrics. With the evolution of deep learning it has gained more lime-light. Here is the exection time comparison of vectorised and non vectorised code. # initialization of array import numpy as np a=np.array([1,2,3,4]) print(a) [1 2 3 4] #initialize numpy array import time A = np.random.rand(1000000) B = np.random.rand(1000000) # calculating execution time using vectorization tic = time.time() C =np.dot(A, B) toc =time.time() print('total time taken in vectorised multiplication' + str(toc-tic) + 'mili-seconds') total time taken in vectorised multiplication0.002000093460083008mili-seconds # calculating execution time using non vectorization code tic = time.time() for i in range(1000000): C = C +A[i]*B[i] print(C) toc= time.time() print('total time taken in non-vectorised code'+ str(toc-tic) +' mili seconds') 749296...