(Python Example for Beginners)
Write a NumPy program to compute the inner product of vectors for 1-D arrays (without complex conjugation) and in higher dimension.
Sample Solution :
Python Code :
import numpy as np a = np.array([1,2,5]) b = np.array([2,1,0]) print("Original 1-d arrays:") print(a) print(b) print result = np.inner(a, b) print("Inner product of the said vectors:") x = np.arange(9).reshape(3, 3) y = np.arange(3, 12).reshape(3, 3) print("Higher dimension arrays:") print(x) print(y) result = np.inner(x, y) print("Inner product of the said vectors:") print(result)
Original 1-d arrays: [1 2 5] [2 1 0] Inner product of the said vectors: Higher dimension arrays: [[0 1 2] [3 4 5] [6 7 8]] [[ 3 4 5] [ 6 7 8] [ 9 10 11]] Inner product of the said vectors: [[ 14 23 32] [ 50 86 122] [ 86 149 212]]
Python Example – Write a NumPy program to evaluate Einstein’s summation convention of two given multidimensional arrays
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
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