Is there any difference? If not, what is preferred by convention? The performance seems to be almost the same.
a=np.random.rand(1000,1000)
b=np.random.rand(1000,1000)
%timeit a.dot(b) #14.3 ms ± 374 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit np.dot(a,b) #14.7 ms ± 315 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit a @ b #15.1 ms ± 779 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
They are all basically doing the same thing. In terms of timing, based on Numpy's documentation here:
If both a and b are 1-D arrays, it is inner product of vectors (without complex conjugation).
If both a and b are 2-D arrays, it is matrix multiplication, but
using matmul or a @ b is preferred.
If either a or b is 0-D (scalar), it is equivalent to multiply and
using numpy.multiply(a, b) or a * b is preferred.
If a is an N-D array and b is a 1-D array, it is a sum product over
the last axis of a and b.