Gradient descent method
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The gradient descent method is an optimization algorithm that finds the values of parameters that minimize a cost function, particularly when analytical solutions are not feasible. It involves iteratively adjusting coefficients to lower the cost, visualized as reaching the bottom of a bowl representing the cost function. Ultimately, with sufficient iterations, this process leads to identifying the best coefficient values for further analysis.
Gradient descent method
- 2. GRADIENT DESCENT METHOD: •Gradient descent is an optimization algorithm used to find the values of parameters (coefficients) of a function (f) that minimizes a cost function (cost). •Gradient descent is best used when the parameters cannot be calculated analytically (e.g. using linear algebra) and must be searched for by
- 3. THINK OF IT LIKE THIS … EVENTUALLY THE WOOD WILL REACH A POINT OF MINIMUM THICKNESS
- 4. ANOTHER EXAMPLE • Think of a large bowl like what you would eat cereal out of or store fruit in. This bowl is a plot of the cost function (f). • A random position on the surface of the bowl is the cost of the current values of the coefficients (cost). • The bottom of the bowl is the cost of the best set of coefficients, the minimum of the function. • The goal is to continue to try different values for the coefficients, evaluate their cost and select new coefficients that have a slightly better (lower) cost. • Repeating this process enough times will lead to the bottom of the bowl and you will know the values of the
- 6. IN THEORY THIS MEANS THAT AFTER APPLYING ENOUGH ITERATIONS OF THE PROCESS TO A DATA SET WE COULD SEE A FINAL CLOSEST MINIMUM COST FUNCTION TO BASE FURTHER WORK ON.
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