A STUDY ON OPTIMIZATION ALGORITHMS IN MACHINE LEARNING
Keywords:
Optimization Algorithms, Machine Learning, Gradient Descent, Adam, Stochastic Gradient Descent, Model Convergence, Loss Function, Hyperparameter Tuning, Convergence Speed, Optimization Challenges.Abstract
Optimization algorithms play a pivotal role in the field of machine learning, as they are responsible for minimizing the loss function and ensuring that the model converges to the best possible solution. The effectiveness of machine learning models heavily relies on the choice and tuning of these optimization algorithms. This paper provides an in-depth analysis of the most widely used optimization algorithms in machine learning, such as Gradient Descent, Stochastic Gradient Descent (SGD), Adam, and their variants. By reviewing the strengths, weaknesses, and practical implications of these algorithms, this study aims to provide insights into how to select the optimal optimizer for various machine learning tasks. Furthermore, we discuss the challenges faced during optimization and highlight emerging trends in the optimization landscape.
