Stochastic gradient descent (SGD) is a widely used optimization algorithm in machine learning and deep learning. At its core, SGD is about efficiently finding the best set of parameters (like weights in a neural network) that minimize a loss function, which measures how far off a model‘s predictions are from the actual values. The word “stochastic” refers to the fact that the algorithm uses randomness in its process, making optimization faster and less memory-intensive compared to traditional gradient descent.
In classic or “batch” gradient descent, the algorithm calculates the gradient (the direction and rate of steepest ascent or descent) of the loss function using the entire training dataset at once. While this can lead to accurate updates, it’s often computationally expensive and slow, especially with large datasets. SGD offers a practical alternative by updating the model parameters using only a single training example (or a small mini-batch) at each step. This makes each update much faster and allows the algorithm to start learning and improving the model almost immediately.
Here’s how it works: For each iteration, SGD picks a random data point (or a random subset, called a mini-batch) from the training set. It computes the gradient of the loss function with respect to the model‘s parameters, based only on this small sample. Then, it adjusts the parameters in the direction that reduces the loss, scaled by a factor called the learning rate. Because each step is based on just a small slice of the data, the updates are noisy or “stochastic.” This noise can actually help the model avoid getting stuck in local minima (suboptimal solutions), improving its chances of finding a better overall minimum of the loss function.
SGD is a foundational technique in training neural networks and many other machine learning models. It is particularly well-suited for large-scale and online learning settings, where data arrives in streams or is too big to process all at once. Variants of SGD, such as adding momentum or using adaptive learning rates, are commonly used to further accelerate and stabilize training.
However, SGD does require careful tuning of hyperparameters, especially the learning rate. Too high a learning rate can cause the algorithm to diverge, while too low can make convergence painfully slow. Despite its simplicity, SGD’s effectiveness and scalability have made it the default optimizer for many deep learning frameworks and applications.
