![]() This process is repeated until the cost function becomes 0 or very close to 0.You need to make sure that this learning rate is not too high nor too low.Ĭoefficient = coefficient – (alpha * del) A learning rate (alpha) can be selected to control how much these coefficients will change in each iteration. After knowing the direction of downhill from the slope, you update the coefficient values accordingly.The direction should be such that you get a lower cost(error) in the next iteration. Calculating the slope will help you to figure out the direction to move the coefficient values. We know from the concept of calculus that the derivative of a function is the slope of the function.The cost function is calculated by putting this value of the coefficient in the function.The calculation of gradient descent begins with the initial values of coefficients for the function being set as 0 or a small random value. Read: Boosting in Machine Learning: What is, Functions, Types & Features Gradient Descent Algorithm- Methodology However, these bottoms may not be the lowest points and are known as local minima. Depending on the start position of the ball, it may rest on many bottoms of the valley. You want the ball to reach the bottom of the valley, where the bottom of the valley represents the least cost function. The valley is the plot for the cost function here. You can imagine gradient descent as a ball rolling down a valley. This step is repeated until the best coefficients are found. Different values are used as the coefficients to calculate the cost function. The bottom of the bowl is the best coefficient for which the cost function is minimum. This bowl is the plot for the cost function. Suppose you have a large bowl similar to something you’ve your fruit in. Source The intuition behind the Gradient Descent algorithm The point at which cost function is minimum is known as global minima. Gradient descent is one such optimization algorithm used to find the coefficients of a function to reduce the cost function. To achieve this goal, you need to find the required parameters during the training of your model. ![]() The goal is to reduce the cost function so that the model is accurate.
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