| Keyword | CPC | PCC | Volume | Score | Length of keyword |
|---|---|---|---|---|---|
| gradient descent with linear regression | 0.71 | 0.8 | 557 | 48 | 39 |
| gradient | 1.97 | 0.1 | 8224 | 12 | 8 |
| descent | 0.63 | 0.9 | 5673 | 86 | 7 |
| with | 0.57 | 0.7 | 9683 | 24 | 4 |
| linear | 0.09 | 1 | 9947 | 47 | 6 |
| regression | 1.21 | 0.5 | 5990 | 17 | 10 |
| Keyword | CPC | PCC | Volume | Score |
|---|---|---|---|---|
| gradient descent with linear regression | 1.53 | 0.3 | 6902 | 60 |
| linear regression using gradient descent | 0.71 | 0.3 | 6794 | 86 |
| linear regression gradient descent python | 0.59 | 0.3 | 7114 | 33 |
Gradient descent is simply used in machine learning to find the values of a function's parameters (coefficients) that minimize a cost function as far as possible. You start by defining the initial parameter's values and from there gradient descent uses calculus to iteratively adjust the values so they minimize the given cost-function.
How is local minima possible in gradient descent?The key intuition from gradient descent is that it takes the fastest route towards the minimum point from each step to converge fast. It is done by taking the partial derivative at each step to find the direction towards the local minimum. The graph below explains the intuition for a function with only one parameter.
