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.
GAMCO is a pioneer in the creation of Artificial Intelligence and Machine Learning software solutions. GAMCO's solutions are designed to [....]
Read More »The rise of Artificial Intelligence (AI) in business is very topical. Its use is spreading and is changing, even, the models [...]
Read More »The content of this article synthesizes part of the chapter "Concept and brief history of Artificial Intelligence" of the thesis Generation of Artificial [...]
Read More »The commercial optimization software based on artificial intelligence must have feedback of the commercial actions carried out, of the nu [...]
Read More »
