| Keyword | CPC | PCC | Volume | Score |
|---|---|---|---|---|
| gradient descent machine learning ppt | 0.67 | 0.6 | 746 | 40 |
| what is gradient descent in machine learning | 1.53 | 0.1 | 6600 | 71 |
| types of gradient descent in machine learning | 0.88 | 1 | 9907 | 83 |
| gradient descent in machine learning medium | 0.34 | 0.9 | 8169 | 84 |
| is gradient descent a machine learning model | 1.18 | 0.6 | 6755 | 83 |
| gradient descent in machine learning code | 1.81 | 0.1 | 7778 | 30 |
| gradient descent machine learning algorithm | 0.7 | 0.4 | 4844 | 47 |
| gradient descent formula in machine learning | 0.53 | 0.5 | 8827 | 70 |
| gradient descent learning algorithm ppt | 1.21 | 1 | 4126 | 1 |
| gradient descent machine learning python | 1.18 | 0.7 | 1186 | 9 |
| descente de gradient machine learning | 0.21 | 0.8 | 2587 | 34 |
| batch gradient descent in machine learning | 1.98 | 0.5 | 4273 | 97 |
| gradient in machine learning | 1.93 | 0.6 | 4135 | 63 |
| gradient descent and learning rate | 1.36 | 0.7 | 1456 | 27 |
| gradient descent learning rate | 1.16 | 1 | 6038 | 89 |
| gradient descent in deep learning | 0.31 | 0.2 | 4916 | 34 |
| what is a gradient in machine learning | 0.78 | 0.2 | 6036 | 59 |
| gradient descent lecture notes | 0.95 | 0.9 | 390 | 86 |
| gradients in machine learning | 1.94 | 0.8 | 1020 | 58 |
Gradient is a commonly used term in optimization and machine learning. For example, deep learning neural networks are fit using stochastic gradient descent, and many standard optimization algorithms used to fit machine learning algorithms use gradient information. In order to understand what a gradient is, you need to understand what a derivative is from the field of calculus.
Can you please explain the gradient descent?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.
