<ul><li>Gradient descent is crucial in decreasing the loss function of neural networks for accuracy by finding the gradient of the loss function with respect to the weights.</li><li>Stochastic Gradient Descent (SGD) in batches of training data makes the descent less accurate yet more efficient compared to other optimizers like Adam, which is an adaptive learning rate optimizer.</li><li>In testing the effectiveness on MNIST handwritten digits dataset, SGD shows roughly 91% accuracy in 43 seconds, outperforming non-SGD models with an average accuracy of 87.47% in 54 seconds.</li><li>Despite being faster than regular gradient descent, SGD falls short compared to optimizers like Adam and RMSprop, which show higher accuracies and similar training times.</li></ul>

Effectiveness of Stochastic Gradient Descent in Multilayer Perceptrons

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