Stochastic Gradient Descent
Stochastic Gradient Descent (SGD) is an iterative optimization algorithm used in machine learning and deep learning to minimize loss functions by updating model parameters based on the gradient computed from a single training example or a small batch. It is a variant of gradient descent that introduces randomness by sampling data points, making it computationally efficient for large datasets. SGD is fundamental for training neural networks and other models where the objective function is defined as a sum over many data points.
Developers should learn SGD when working with large-scale machine learning problems, such as training deep neural networks on massive datasets, where computing the full gradient over all data points is computationally prohibitive. It is particularly useful in online learning scenarios where data arrives in streams, and models need to be updated incrementally. SGD's stochastic nature also helps escape local minima and can lead to faster convergence in practice compared to batch gradient descent.