Stochastic Gradient Descent (SGD) is an optimisation algorithm used in training machine learning models.
The main idea behind stochastic gradient descent is to minimise a cost function by iteratively adjusting model parameters based on feedback from the training data set. Instead of updating the model parameters exhaustively with all the training data at each iteration (as in regular gradient descent), stochastic gradient descent uses small random samples of the training data (known as "minibatches" or "batches") at each iteration to update the model parameters.
Stochastic gradient descent is especially useful when the training data set is very large, as it allows the model to train more efficiently by processing small samples of the data at a time. In addition, stochastic gradient descent can help prevent the model from becoming trapped in local optima and converge more quickly to a global optimum.
Stochastic gradient descent is a widely used algorithm in the optimisation of deep learning models, and is essential for training large neural networks that require large training data sets.
Typically, Machine Learning is used to solve business problems in various sectors and areas where different algorithms are applied.
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