What is Gradient Decent in Machine Learning? _ Day 7
Mastering Gradient Descent in Machine Learning Mastering Gradient Descent: A Comprehensive Guide to Optimizing Machine Learning Models Gradient Descent is a foundational optimization algorithm used in machine learning to minimize a model’s cost function, typically Mean Squared Error (MSE) in linear regression. By iteratively adjusting the model’s parameters (weights), Gradient Descent seeks to find the optimal values that reduce the prediction error. What is Gradient Descent? Gradient Descent works by calculating the gradient (slope) of the cost function with respect to each parameter and moving in the direction opposite to the gradient. This process is repeated until the algorithm converges to a minimum point, ideally the global minimum, where the cost function is minimized. Types of Learning Rates in Gradient Descent: Too Small Learning Rate Slow Convergence: A very small learning rate makes the algorithm take tiny steps toward the minimum, resulting in a long training process. High Precision: Useful when fine adjustments are needed to avoid overshooting the minimum, but impractical for large-scale problems due to time inefficiency. Too Large Learning Rate Risk of Divergence: A large learning rate can cause the algorithm to overshoot the minimum, leading to oscillations or divergence where the cost function increases instead of...
