| Keyword | CPC | PCC | Volume | Score |
|---|---|---|---|---|
| explain gradient descent and its types | 1.68 | 0.5 | 6402 | 52 |
| what is gradient in gradient descent | 0.54 | 0.9 | 5985 | 99 |
| gradient descent without a gradient | 0.65 | 0.2 | 8287 | 47 |
| what do you mean by gradient descent | 0.27 | 0.8 | 8292 | 79 |
| use of gradient descent | 1.32 | 0.5 | 6320 | 76 |
| what is the use of gradient descent | 0.93 | 0.8 | 2766 | 34 |
| gradient descent for classification | 1.87 | 0.4 | 4519 | 8 |
| gradient descent is a process of | 0.72 | 0.2 | 4722 | 72 |
| variants of gradient descent | 1.87 | 0.8 | 2891 | 95 |
| gradient descent step by step example | 0.53 | 0.9 | 4963 | 79 |
| gradient descent is used to find | 1.42 | 0.6 | 9605 | 5 |
| gradient descent in maths | 1.32 | 0.6 | 3083 | 25 |
| what does gradient descent do | 0.37 | 0.2 | 3771 | 89 |
| gradient descent method example | 0.4 | 0.9 | 4130 | 48 |
| gradient descent example questions | 0.29 | 0.4 | 7695 | 25 |
| derivation for gradient descent | 0.7 | 1 | 9309 | 67 |
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.
Is gradient really the direction of steepest ascent?We can interpret this as saying that the gradient,rf(a), has enough information to nd the deriva-tive in any direction. Steepest ascent. The gradientrf(a) is a vectorin a certain direction. Letube any direction, thatis, any unit vector, and letbe the angle betweenthe vectorsrf(a) andu. Now, we may concludethat the directional derivative
