Mini Batch Gradient Descent

Mini-batch gradient descent (MBGD) is an optimization algorithm used to train machine learning models efficiently by processing data in smaller batches, balancing computational cost with convergence speed. Current research focuses on improving MBGD's robustness to outliers and noisy data, exploring its theoretical convergence properties in various settings (e.g., non-convex problems, high-dimensional data), and developing novel algorithms that leverage MBGD within specific model architectures like graph neural networks and fuzzy logic systems. These advancements enhance the scalability and performance of machine learning across diverse applications, from large-scale clustering and recommendation systems to causal inference and deep learning model training.