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Gradient Descent with Momentum

Unsolved
Fundamentals
Neural Networks
Optimization

Difficulty: 2 | Problem written by zeyad_omar
Problem reported in interviews at

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Gradient descent with momentum is an enhanced version of the gradient descent algorithm where the current gradient or step is the weighted sum of the previous gradients.

In this problem, you are asked to return the gradient given:

grads: 1D vector representing the current (postion 0) and the previous gradients (postion [1:end])
beta: the weight of each component


You can use this formula:

Sample Input:
<class 'list'>
grads: [0.9, 1, 0.88, 0.84, 0.89]
<class 'float'>
beta: 0.5

Expected Output:
<class 'float'>
1.780625

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