| Keyword | CPC | PCC | Volume | Score |
|---|---|---|---|---|
| gradient descent example by hand | 0.48 | 0.5 | 3617 | 92 |
| gradient descent simple example | 0.83 | 0.3 | 3301 | 2 |
| gradient descent step by step example | 1.36 | 0.1 | 8794 | 87 |
| example of gradient descent | 1.16 | 0.4 | 3853 | 59 |
| gradient descent method example | 1.01 | 1 | 1301 | 98 |
| gradient descent solved example | 0.26 | 0.6 | 3808 | 12 |
| real life example of gradient descent | 0.1 | 0.4 | 4722 | 49 |
| gradient descent numerical example | 0.8 | 0.4 | 2795 | 40 |
| gradient descent example questions | 1.71 | 0.3 | 3576 | 3 |
| gradient descent explained simply | 1.4 | 1 | 656 | 80 |
| gradient descent and its types | 1.13 | 0.8 | 116 | 22 |
| use of gradient descent | 1.24 | 0.1 | 7928 | 80 |
| gradient descent calculation example | 0.65 | 0.5 | 9347 | 4 |
| explain gradient descent and its types | 0.01 | 0.2 | 3300 | 44 |
| gradient descent in maths | 1.82 | 0.6 | 7357 | 91 |
| types of gradient descent | 0.93 | 0.7 | 6038 | 24 |
| what is the gradient descent | 0.67 | 0.4 | 4676 | 70 |
| what is the use of gradient descent | 1.5 | 0.6 | 622 | 38 |
| application of gradient descent | 1.95 | 0.1 | 384 | 6 |
| the gradient descent method | 0.21 | 0.8 | 1830 | 87 |
| different types of gradient descent | 0.73 | 0.2 | 6973 | 6 |
| when to use gradient descent | 1.16 | 0.1 | 5683 | 65 |
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
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.
