Gradient Descent
Gradient descent is an iterative optimization algorithm used to minimize a function by moving in the direction of the steepest descent, as defined by the negative of the gradient. It is a fundamental technique in machine learning and deep learning for training models by adjusting parameters to reduce loss or error. The algorithm updates parameters repeatedly until convergence to a local or global minimum.
Developers should learn gradient descent when working on machine learning projects, as it is essential for training models like linear regression, neural networks, and support vector machines. It is particularly useful for large-scale optimization problems where analytical solutions are infeasible, enabling efficient parameter tuning in applications such as image recognition, natural language processing, and predictive analytics.