Gradient-based Methods
Gradient-based methods are optimization algorithms used in machine learning and data science to find the minimum of a cost function by iteratively adjusting the parameters of a model. These methods use the gradient of the cost function with respect to the model parameters to determine the direction of steepest descent and update the parameters accordingly. The most commonly used gradient-based method is stochastic gradient descent (SGD), which randomly selects a subset of the training data (mini-batch) to compute the gradient and update the parameters. Other popular gradient-based methods include Adam, Adagrad, and RMSprop, which adapt the learning rate based on the history of gradients and parameters. Gradient-based methods are widely used in deep learning for training neural networks with millions of parameters.
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