This process can be explained mathematically using the gradient. He can do it as long as he is not near the peak.Īs he gets closer to the peak, his steps will get smaller and smaller to prevent overshooting it. He may start climbing the hill by using really big steps in the steepest direction. Mathematically, a gradient is a partial derivative with concerning idea inputs.Īssume a blindfolded man who willing to reach the top of a hill with the fewer steps along the way as possible. Model stop learning, if the slope is zero. If the gradient is high model can learn fast. The slope will be steeper if the gradient is high. We can also consider a gradient as the slope of a particular function. A gradient determines the change in all weights concerning the change in residual. Gradient determines up to what extent the output of a function changes if we change the inputs. To reduce the objective function, the most optimal value of the parameters of the function is used. Loss function, simply talking, is the type of the squared difference between actual values and predicted values. It’s based on a convex function and twitches its parameters iteratively to minimize a given function to its local minimum. The gradient descent is used to find the most optimal value of parameters/weights which reduces the loss function. It is the loss function that is optimized (minimized). The function which is set to be reduced is called an objective function. Gradient descent algorithm is an optimization algorithm that is used to reduce the cost function. Let us discuss in a much detailed manner. For a data scientist, it is important to get a solid understanding of the concepts of the gradient descent algorithm. It is essentially used for tuning the parameters of the learning model. ![]() Gradient Descent is used when training data models, can be linked with all algorithms, and is easy to learn and execute. I discussed assumptions of Logistic regression and cross-entropy loss in my previous articles. Upvote 2+ In this article, I will do a complete analysis of the gradient descent algorithm. Gradient Descent is an optimization algorithm worked for reducing the loss function in multiple machine learning algorithms.
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