Mini-Batch Gradient Descent
Mini-Batch Gradient Descent is an optimization algorithm used in machine learning and deep learning to train models by iteratively updating parameters based on gradients computed from small subsets (mini-batches) of the training data. It strikes a balance between the computational efficiency of Stochastic Gradient Descent (SGD) and the stability of Batch Gradient Descent, making it widely used for large datasets. This approach reduces noise in gradient estimates compared to SGD while being more memory-efficient and faster than full-batch methods.
Developers should learn Mini-Batch Gradient Descent when training machine learning models on large datasets, as it offers a practical compromise between speed and convergence stability, especially in deep learning applications like neural networks. It is essential for scenarios where memory constraints prevent loading the entire dataset at once, such as in image recognition or natural language processing tasks, and it often leads to faster training times and better generalization than pure SGD or batch methods.