In machine learning, there are two types of parameters: those learned automatically by the machine and those manually set by data scientists or machine learning engineers, known as hyper-parameters.
Machine-learnable parameters: These are the parameters that an algorithm autonomously identifies and adjusts throughout the training process for a specific dataset.
Hyper-parameters, on the other hand, require specific values assigned by data scientists or machine learning engineers. These values help optimize how algorithms learn, influencing the model’s overall performance.
A crucial hyper-parameter in this context is the learning rate, represented by the symbol α. It controls the speed at which an algorithm updates, or learns, the values of the parameters it estimates. To put it differently, the learning rate influences how the neural network's weights are adjusted in relation to the loss gradient. This means it dictates the frequency at which the neural network updates the concepts it has learned.
Effect of Learning Rate
Through examples in the training dataset, a neural network learns, or approximates, a function to map inputs to outputs beneficially. The speed, or the rate at which this learning takes place, is regulated by the learning rate.
The learning rate guides how much allocated error is used to update the model's weights every time they are modified. For instance, this happens at the end of each batch of training data instances.
If the learning rate is ideally set, the model learns to estimate the function as best as possible given the available resources, such as the number of layers and nodes per layer in a specified number of training epochs or runs through the training data.
A good learning rate is low enough to ensure the network can reach useful outcomes, yet high enough to complete training in a reasonable timeframe. A smaller learning rate might need more training epochs due to fewer modifications, while a larger learning rate can result in quicker changes.
Important to note, larger learning rates might end up giving suboptimal final weight sets. We can't calculate a neural network's weights analytically; rather, these weights need to be identified using stochastic gradient descent, an optimization method based on experience. Simply put, this method is commonly used for training deep learning rate neural networks.
Algorithms and Adaptive Learning Rate
Adaptive learning rate allows the training algorithm to keep track of the model’s performance, making adjustments to the learning rate to optimize results.
Based on the gradient value of the cost function, the learning rate increases or decreases. When the gradient value is high, the learning rate is lowered, and when the gradient value is low, the learning rate is raised. Hence, the learning pace slows or quickens corresponding to steeper or shallower areas of the cost function curve.
Approaches used to regulate learning rates usually outperform fixed AI learning rates. One such widely used approach is the adaptive learning rate in machine learning, especially when building deep neural networks using stochastic gradient descent.
A variety of learning rate methodologies are available, such as:
- Decaying Learning Rate: In this technique, the learning rate reduces as the number of epochs/iterations increase.
- Scheduled Drop Learning Rate: Unlike the decaying strategy where the learning rate drops continually, this method involves lowering by a defined fraction at set intervals.
- Cycling Learning Rate: In this method, the learning rate oscillates between a base rate and a maximum rate in a cyclical manner. The learning rate fluctuates at a constant rate between these value boundaries in a triangular pattern.
The Gradient Descent Method is a popular strategy for optimizing parameters in machine learning. When training a model, each parameter is initially set or given random values. These initial values are used to create the cost function, gradually improving estimates so that, over time, the cost function reaches its minimum value.