I encountered an error while attempting to multiply two numpy arrays with different dimensions in my data science project. Could anyone kindly share insights on the correct ways to multiply matrices with disparate dimensions in the context of numpy arrays? Your assistance with a code snippet would be greatly appreciated. Thank you!

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Yes, there is a way by which you can multiply matrices of different shapes and the method is known as Broadcasting. I am attaching below an example code that implements this technique on two matrices of different shapes:

- This code uses broadcasting to multiply matrices
`a`

and`b`

along the last axis, resulting in a new array`c`

with dimensions`(5, 5, 3)`

. - The resulting shape of
`c`

indicates that the last axis of`b`

was broadcasted (repeated) to match the third axis of`a`

.

Yes, you can multiply arrays of different dimensions using NumPy’s `.reshape()`

method. It will first reshape the arrays to match their dimensions and then apply multiplication. A sample code is provided below for your understanding.

This code generates a 3D array ‘a’ with shape (5,5,3) and a 2D array ‘b’ with shape (5,5). It then reshapes ‘b’ to (5,5,1) and multiplies it element-wise with ‘a’, resulting in a new array ‘c’ with dimensions (5,5,3).