Learning rate is a hyperparameter used in machine learning algorithms that controls how much the model changes in response to the estimated error each time the model weights are updated. It is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. The learning rate is a configurable hyperparameter used in the training of neural networks that has a small positive value, often in the range between 0.0 and 1.0.

The learning rate controls how quickly the model is adapted to the problem. Smaller learning rates require more training epochs given the smaller changes made to the weights each update, whereas larger learning rates result in rapid changes and require fewer training epochs. However, a learning rate that is too large can cause the model to converge too quickly to a suboptimal solution, whereas a learning rate that is too small can cause the process to get stuck.

The learning rate is related to the step length determined by inexact line search in quasi-Newton methods and related optimization algorithms. There is a trade-off between the rate of convergence and overshooting when setting a learning rate. A too high learning rate will make the learning jump over minima, but a too low learning rate will either take too long to converge or get stuck in an undesirable local minimum.

Adaptive learning rates frequently beat fixed AI learning rates in neural networks. An adaptive learning rate in machine learning is commonly utilized when using stochastic gradient descent to build deep neural nets. There are various sorts of learning rate approaches, such as decaying learning rate, which drops the learning rate as the number of epochs/iterations increases.

In summary, the learning rate is a hyperparameter that controls the step size at each iteration while moving toward a minimum of a loss function. It regulates the weights of a neural network concerning the loss gradient and indicates how often the neural network refreshes the notions it has learned. The choice of the value for learning rate can impact how fast the algorithm learns and whether the cost function is minimized or not.