Month: April 2018
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Intro To Matrices With Python And Numpy
Matrices With Numpy Numpy is really good at working with matrix mathematics, and matrix math is everywhere in data science. It will save you a lot of time if you get acquainted with the numpy library. Okay, without further ado, let’s get started by importing numpy and creating a sample matrix to work with.
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How To Create Vectors And Matrices With Numpy
Numpy Vectors And Matrices Numpy vectors and matrices will show up all the time in data science and machine learning. So, let’s take a look at how to create them. Let’s first import numpy. import numpy as np Next, let’s try to create our first vector row. np.array([1,2,3,4]) # array([1, 2, 3, 4]) Wasn’t
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How To Add/Subtract With Numpy Matrices
Adding/Subtracting With Matrices Adding and subtracting matrices is a fundamental part of linear algebra and a lot of data science techniques. Let’s take a look at how to add/subtract with numpy arrays and matrices. First, let’s import numpy and create some matrices to practice our operations with. import numpy as np a = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]])
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How To Calculate Dot Product With Numpy
Numpy dot product Dot product is a common linear algebra matrix operation to multiply vectors and matrices. It is commonly used in machine learning and data science for a variety of calculations. It can be simply calculated with the help of numpy. This post will go through an example of how to use numpy
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How To Calculate Eigenvectors And Eigenvalues With Numpy
Eigenvectors and eigenvalues with numpy In machine learning, eigenvectors and eigenvalues come up quite a bit. They are used in a variety of data science techniques such as Principal Component Analysis for dimensionality reduction of features. Let’s take a look at how to calculate these linear algebra values efficiently with Numpy, a popular python
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