NumPy is an open source library available in Python that aids in mathematical, scientific, engineering, and data science programming. NumPy is an incredible library to perform mathematical and statistical operations. It works perfectly well for multi-dimensional arrays and matrices multiplication
For any scientific project, NumPy is the tool to know. It has been built to work with the N-dimensional array, linear algebra, random number, Fourier transform, etc. It can be integrated to C/C++ and Fortran.
NumPy is a programming language that deals with multi-dimensional arrays and matrices. On top of the arrays and matrices, NumPy supports a large number of mathematical operations. In this part, we will review the essential functions that you need to know for the tutorial on 'TensorFlow.'
NumPy is memory efficiency, meaning it can handle the vast amount of data more accessible than any other library. Besides, NumPy is very convenient to work with, especially for matrix multiplication and reshaping. On top of that, NumPy is fast. In fact, TensorFlow and Scikit learn to use NumPy array to compute the matrix multiplication in the back end.
|Tutorial||How to Install NumPy|
|Tutorial||Python Numpy Array Tutorial|
|Tutorial||numpy.zeros() and numpy.ones() in Python|
|Tutorial||numpy.reshape() in Python|
|Tutorial||numpy.hstack() and numpy.vstack() in Python|
|Tutorial||numpy.asarray() in Python|
|Tutorial||numpy.arange() in Python|
|Tutorial||numpy.linspace() and numpy.logspace() in Python|
|Tutorial||Indexing and Slicing NumPy Arrays in Python|
|Tutorial||NumPy Statistical Functions with Example|
|Tutorial||numpy.dot(): Dot Product in Python using Numpy|
|Tutorial||NumPy Matrix Multiplication with np.matmul() Example|