The difference between numpy.random.rand() and numpy.random.randn()

When building a deep network manually, we generally usenumpy.random.randn()To initialize the parameters. Thennumpy.random.randn()withnumpy.random.rand()what differences are there?

import numpy as np
a =  np.random.rand(4, 1)
b = np.random.randn(4, 1)
print(a)
print("\n")
print(b)

import numpy as np
a =  np.random.rand(4, 1)
b = np.random.randn(4, 1)
print(a)
print("\n")
print(b)

Result:
[[0.98429255]
 [0.25342119]
 [0.26999272]
 [0.24893671]]


[[ 0.07078823]
 [-0.54274356]
 [ 0.29564877]
 [-2.00665922]]

numpy.random.rand()Generated from $[0,1)$ There is no negative value between the random numbers.
numpy.random.randn()Producing a random number that follows a normal distribution will result in a negative value.

The parameters in deep learning are likely to have negative values, so we don't use them.numpy.random.rand()。

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