Mini-Batch Gradient Descent

Mini-Batch Gradient Descent is an optimization algorithm used in machine learning and deep learning to minimize the loss function of a model. It combines the benefits of both stochastic gradient descent and batch gradient descent by dividing the training dataset into small batches. Each batch is used to update the model parameters, which helps in reducing the variance of the parameter updates and leads to faster convergence. This method is particularly useful in scenarios where the dataset is large, as it allows for efficient computation and better generalization. Common use cases include training neural networks and other machine learning models where iterative optimization is required.