Stochastic Gradient Descent (SGD) is a popular optimization algorithm used in machine learning and deep learning. It is a variant of the gradient descent algorithm that is more computationally efficient and can handle large datasets.
SGD updates the parameters of a model by calculating the gradient of the loss function for each data point or a small batch of data points, rather than the entire dataset. This makes it faster and more scalable for large datasets.
One of the key advantages of SGD is that it converges faster compared to traditional gradient descent algorithms. However, it can be sensitive to the learning rate and may require careful tuning.
SGD is widely used in training neural networks and other machine learning models, and many popular deep learning frameworks, such as TensorFlow and PyTorch, provide implementations of SGD.
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