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NumPy is an open source library available in Python, which helps in mathematical, scientific, engineering, and data science programming. It is a very useful library to perform mathematical and statistical operations in Python. It works perfectly for multi-dimensional arrays and matrix multiplication. It is easy to integrate with C/C++ and Fortran. 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.

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👉 Python is an open source library available in Python, which helps in mathematical, scientific, engineering, and data science programming.

👉 numpy.zeros() or np.zeros Python function is used to create a matrix full of zeroes.

👉 numpy.ones() in Python can be used when you initialize the weights during the first iteration in TensorFlow and other statistic tasks.

👉 Python NumPy Reshape function is used to shape an array without changing its data.

👉 Python NumPy Flatten function is used to return a copy of the array in one-dimension.

👉 Numpy.hstack is a function in Python that is used to horizontally stack sequences of input arrays in order to make a single array.

👉 Numpy.vstack is a function in Python which is used to vertically stack sequences of input arrays in order to make a single array.

👉 numpy.arange() is an inbuilt numpy function that returns an ndarray object containing evenly spaced values within a defined interval.

👉 Numpy.dot product is a powerful library for matrix computation.

👉 The Numpy matmul() function is used to return the matrix product of 2 arrays.

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