Mini-Batch Gradient Descent
Mini-batch gradient descent is a widely used optimization algorithm in machine learning and deep learning, combining the benefits of both batch gradient descent and stochastic gradient descent. Introduced conceptually in the 1980s, it processes small, randomly selected subsets of the training dataset, called mini-batches, to compute the gradient and update model parameters. This method strikes a balance between the computational efficiency of batch gradient descent and the noise-reduction capabilities of stochastic gradient descent, leading to faster convergence and better generalization in training neural networks.
https://en.wikipedia.org/wiki/Stochastic_gradient_descent
The key advantage of mini-batch gradient descent is its ability to leverage the parallel processing capabilities of modern hardware, such as GPUs and TPUs. By processing mini-batches instead of individual samples, the algorithm optimizes resource usage while maintaining stability in the gradient updates. The size of the mini-batch is a crucial hyperparameter, as smaller batches can introduce higher variability, potentially improving the model's ability to escape local minima, while larger batches reduce noise and improve computational throughput. Libraries like PyTorch and TensorFlow provide built-in support for mini-batch processing, enabling developers to fine-tune batch sizes for optimal performance.
https://pytorch.org/docs/stable/generated/torch.optim.SGD.html
Mini-batch gradient descent is highly versatile and is applied in various machine learning tasks, including image classification, natural language processing, and reinforcement learning. It is particularly effective in handling large-scale datasets, as it reduces memory overhead and enables distributed training. Techniques such as adaptive learning rates (e.g., Adam Optimizer) often pair with mini-batch processing to further enhance the efficiency and convergence of the training process. The method’s adaptability and performance make it an essential tool in modern AI and data science workflows.
https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/SGD
https://en.wikipedia.org/wiki/Adam_(optimization_algorithm)