A Comprehensive Overview of Gradient Descent and its Optimization Algorithms

Abstract: This study explores machine learning gradient-based optimization algorithms, highlighting the critical importance of gradient descent and investigating adaptive strategies to improve its performance. The fundamental technique of optimization is gradient descent, although balancing convergence speed and accuracy can be difficult due to gradient descent's reliance on fixed learning rates. The study explores a variety of adaptive learning techniques, including as drop, decay, cyclic learning, and adaptive learning. These techniques are designed to modify learning rates in real-time during optimization, hence affecting stability and convergence. Additionally, the research delves into momentum-based methods like Adam, RMSProp, AdaGrad, and AdaDelta, clarifying their use in reducing the difficulties associated with traditional gradient descent. The study also clarifies gradient clipping methods, addressing the problem of expanding gradients and offering solutions to stabilize and enhance machine learning models. The goal of this thorough investigation is to provide practitioners with a sophisticated grasp of optimization techniques so they may guide machine learning models toward effectiveness, precision, and robustness in a variety of application domains.

Keywords: Gradient Descent, Optimizations, Learning Rate, Adam, Neural Networks