What are some common linear algebra functions in NumPy?
An example of an AI chatbot conversation about NumPy's linear algebra functions.
What are some common linear algebra functions in NumPy?
NumPy provides several functions for linear algebra, including dot(), inv(), eig(), and linalg.solve(). Would you like details on any specific function?
Can you explain how to use the dot() function?
Sure! The dot() function computes the dot product of two arrays. For example, np.dot(a, b) multiplies arrays a and b. If they are 2D, it performs matrix multiplication.
What about the inv() function?
The inv() function computes the inverse of a square matrix. You can use it like this: np.linalg.inv(matrix), where matrix is your square array.
Are there any functions for eigenvalues?
Yes! The eig() function returns the eigenvalues and right eigenvectors of a square array. You can call it using np.linalg.eig(matrix).
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