We deal with two sorts of parameters in machine learning: machine learnable parameters and hyper-parameters.
The learning rate, denoted by the symbol α, is a hyper-parameter used to govern the pace at which an algorithm updates or learns the values of a parameter estimate. In other words, the learning rate regulates the weights of our neural network concerning the loss gradient>. It indicates how often the neural network refreshes the notions it has learned.
From examples in the training dataset, a neural network learns or approximates a function to optimally map inputs to outputs.
The rate of learning or speed at which the model learns is controlled by the hyperparameter. It regulates the amount of allocated error with which the model’s weights are updated each time they are updated, such as at the end of each batch of training instances.
The model will learn to best estimate the function given available resources – the number of layers and nodes per layer in a particular number of training epochs -passes through the training data if the learning rate is perfectly calibrated.
A desirable learning rate is low enough for the network to converge on something useful while yet being high enough to train in a reasonable length of time.
Smaller learning rates necessitate more training epochs because of the fewer changes. On the other hand, larger learning rates result in faster changes.
Moreover, larger learning rates frequently result in a suboptimal final set of weights.
An analytical method cannot be used to calculate the weights of a neural network. Instead, the weights must be discovered using stochastic gradient descent, an empirical optimization approach. In simpler terms, the stochastic gradient descent algorithm is used to train deep learning rate neural networks.
As a result, we should avoid using a learning rate that is either too high or too low. However, we must set up the model so that a decent enough set of weights is determined on average to approximate the mapping issue as represented by the training dataset.
It allows the training algorithm to keep track of the model’s performance and automatically alter the learning rate to achieve optimum results.
The learning rate increases or decreases in this method depending on the cost function’s gradient value.
As a result, learning slows down and speeds up at steeper and shallower regions of the cost function curve, respectively.
The most basic model of this reduces the learning rate once the model’s performance reaches a plateau. The model accomplishes this by reducing the learning rate by a factor of two, or by an order of magnitude. If the performance does not improve, the learning rate might be increased again.
An adaptive learning rate in machine learning is commonly utilized when using stochastic gradient descent to build deep neural nets.
There are, however, various sorts of learning rate approaches: