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 small, randomly selected subsets of data (batches) rather than the entire dataset. It is a variant of gradient descent that introduces randomness to escape local minima and accelerate convergence, making it particularly efficient for large-scale datasets. SGD is fundamental for training neural networks and other models where computational efficiency is critical.
Developers should learn SGD when working on machine learning projects involving large datasets, as it reduces memory usage and speeds up training compared to batch gradient descent. It is essential for training deep neural networks in frameworks like TensorFlow and PyTorch, and is widely used in applications such as image recognition, natural language processing, and recommendation systems. Understanding SGD helps optimize model performance and handle real-world data efficiently.