Introduction
Mini-batch gradient descent is an optimization algorithm which has gained popularity in recent years due to its ability to scale effectively and efficiently to large datasets. This method of training is based on a technique called “stochastic gradient descent”, which in turn is related to gradient descent—a commonly used method of optimization (Konigreich, 2020).
The idea behind mini-batch gradient descent is to split the dataset into smaller batches. Each batch is then used to estimate the gradient, or “slope” of the parameter values at a given iteration. This helps to regularize, or regularise, the training of the algorithm by allowing the data to be updated more quickly and accurately, and it can also help to prevent overfitting.
Features
Mini-batch gradient descent has several advantages compared to both single-batch gradient descent and stochastic gradient descent (SGD). Firstly, it can iterate faster as it is processing data in batches and can also scale better with large datasets. The algorithm is also less prone to overfitting and more easily converges on the optimal solution.
Moreover, mini-batch gradient descent is faster to compute than single-batch gradient descent and SGD. It is much faster than a single-batch gradient descent, which requires the entire dataset to be reprocessed at each iteration. The mini-batch approach reduces the number of iterations required by splitting the data into small batches, thus reducing the computational cost.
Finally, the parameters obtained with mini-batch gradient descent are often less sensitive to the initial parameter values than those obtained with single-batch gradient descent (Konigreich, 2020).
Applications
Mini-batch gradient descent has become increasingly popular in recent years, and is widely used in applications such as natural language processing, computer vision, robotics, and reinforcement learning (Konigreich, 2020).
The algorithm is particularly useful for deep learning applications, as its quicker convergence makes it much more efficient on vast datasets. It is often used in supervised learning problems such as image classification, where an algorithm is trained to recognize patterns in images—for instance, it could be used to determine whether an image is of a cat or a dog.
In addition, mini-batch gradient descent is widely used in unsupervised learning problems, where the algorithm is presented with data without any labels or target values. It is often used to identify meaningful patterns or structures in a dataset.
Conclusion
As concluded, mini-batch gradient descent is an optimization algorithm which has various advantages over both single-batch gradient descent and stochastic gradient descent. It is faster to compute, can iterate faster and scale better with large datasets, and is less prone to overfitting.
The algorithm has become increasingly popular in recent years, and is widely used in applications such as natural language processing, computer vision, robotics, and reinforcement learning. Particularly, its quicker convergence makes it much more efficient on vast datasets, and is often used in both supervised and unsupervised learning problems.
