Gradient descent is a popular optimization algorithm used in machine learning and data science to find the optimal values of parameters in a model. By minimizing the cost function, the model can make more accurate predictions on new data. The learning rate is a crucial hyperparameter in gradient descent that controls the size of the step taken in the direction of the gradient during each iteration, and an appropriate learning rate is necessary for the performance of the algorithm. Despite its effectiveness, gradient descent faces challenges such as vanishing or exploding gradients, curse of dimensionality, local minima and plateaus, overfitting, and computational cost. Various techniques have been developed to address these challenges and gradient descent has a wide range of applications in machine learning, such as linear and logistic regression, neural networks, support vector machines, recommender systems, natural language processing, and image and video analysis. Understanding the principles and applications of gradient descent is essential for developing efficient and effective machine learning models and algorithms.

source update: The Power of Gradient Descent – Towards AI


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