| Keyword | CPC | PCC | Volume | Score | Length of keyword |
|---|---|---|---|---|---|
| gradient descent is an optimization algorithm | 0.08 | 0.4 | 8741 | 28 | 45 |
| gradient | 0.86 | 0.8 | 6754 | 9 | 8 |
| descent | 0.38 | 1 | 8936 | 89 | 7 |
| is | 1.82 | 0.1 | 9849 | 84 | 2 |
| an | 1.29 | 0.5 | 2423 | 44 | 2 |
| optimization | 0.04 | 0.5 | 8020 | 63 | 12 |
| algorithm | 0.45 | 0.1 | 6583 | 72 | 9 |
| Keyword | CPC | PCC | Volume | Score |
|---|---|---|---|---|
| gradient descent is an optimization algorithm | 1.23 | 1 | 4160 | 23 |
| gradient descent optimization algorithms | 1.78 | 0.7 | 4842 | 89 |
| what is gradient descent algorithm | 1.65 | 0.9 | 8107 | 11 |
| an overview of gradient descent optimization | 1.52 | 0.2 | 6156 | 34 |
| gradient descent algorithm wiki | 1.17 | 0.1 | 1034 | 33 |
| gradient descent optimization solved example | 1.67 | 0.9 | 1593 | 24 |
| what does a gradient descent algorithm do | 1.48 | 0.7 | 242 | 61 |
| gradient descent algorithm pdf | 0.34 | 0.4 | 6668 | 52 |
| purpose of gradient descent algorithm | 0.24 | 0.6 | 8180 | 24 |
| how does gradient descent algorithm work | 1.83 | 0.7 | 3936 | 38 |
| gradient descent algorithm with example | 0.01 | 0.7 | 9014 | 30 |
| different gradient descent algorithm | 0.74 | 0.7 | 7577 | 85 |
| best gradient descent algorithm | 1.99 | 0.7 | 9418 | 34 |
| working of gradient descent algorithm | 1 | 0.5 | 438 | 5 |
| principle of gradient descent algorithm | 0.69 | 0.8 | 272 | 82 |
| gradient descent based optimization | 0.4 | 0.5 | 1238 | 30 |
| projected gradient descent algorithm | 1.24 | 1 | 5222 | 33 |
| gradient descent based algorithms | 0.27 | 0.4 | 320 | 53 |
Linear regression does provide a useful exercise for learning stochastic gradient descent which is an important algorithm used for minimizing cost functions by machine learning algorithms. As stated above, our linear regression model is defined as follows: y = B0 + B1 * x.
Does gradient descent work on big data?Gradient Descent is the most common optimization algorithm and the foundation of how we train an ML model. But it can be really slow for large datasets. That’s why we use a variant of this algorithm known as Stochastic Gradient Descent to make our model learn a lot faster.
