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Data Augmentation: Gaussian Noise

Unsolved
Computer Vision

Difficulty: 3 | Problem written by zeyad_omar
Problem reported in interviews at

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In deep learning, the learning process may turn to memorizing. This is known as overfitting, where the model gives high accuracy for the data in which the model was trained but gives terrible accuracy for the unseen data.

One of the techniques used to address this issue is the introduction of some random noise into the dataset to help the model become more generalized.

In this problem you are given an image and the mean and standard deviation of the noise and you are asked to return the image + noise.

*** Please use  np.random.seed(10) to match our test cases.

Sample Input:
<class 'list'>
x: [[1.31, 4.2, 3, 5], [1, 0, 1, 0], [1, 1, 1, 1]]
<class 'int'>
mean: 0
<class 'int'>
sigma: 1

Expected Output:
<class 'numpy.ndarray'>
[[ 2.6415865 4.91527897 1.45459971 4.99161615] [ 1.62133597 -0.72008556 1.26551159 0.10854853] [ 1.00429143 0.82539979 1.43302619 2.20303737]]

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numpy has been already imported as np (import numpy as np)