5 Numpy functions you would need if working on Machine Learning
These 5 functions are some of the multiple out there
Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. Moreover Numpy forms the foundation of the Machine Learning stack.
It is a very important library on which almost every data science or machine learning Python packages such as SciPy (Scientific Python), Mat−plotlib (plotting library), Scikit-learn, etc depends on to a reasonable extent.
1. zeros : np.zeros() Returns an array of given shape (Marix), filled with zeros. Could be used to initialize Filters.
2. fromstring: np.fromstring() Returns a new 1-D constructed array initialized from text data in a string.
3. unique : np.unique() Returns a new array removing the duplicates.
4. transpose : np.transpose() Reverse or permute the axes of an array; returns the matrix transposed.
5. convolve : np.convolve() Returns the discrete, linear convolution of two one-dimensional sequences.
!pip install jovian --upgrade -qimport jovianjovian.commit(project='numpy-array-operations')[jovian] Attempting to save notebook..
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Let's begin by importing Numpy and listing out the functions covered in this notebook.