How do you calculate the learning rate in Machine Learning?

Tiara Williamson
Tiara WilliamsonAnswered

The global hype for its functions has skyrocketed the interest surrounding Machine Learning development and its related fields. The basics of Machine Learning model development and training remain the same. As it progressed, most of the users have shifted their focus to some AI subsets like Deep Learning and Neural Networks. The function of the Machine learning model is to deliver trustworthy data outcomes that can help in future business decision-making predictions.

Models need to be constantly fed with valuable datasets for it to โ€œlearnโ€ and create the pattern for future model processing and deliver highly valuable results. For model data outcome evaluation, we use the hyperparameter called Learning Rate. Learning Rate in Machine Learning is a learning rate parameter that coordinates the optimizer on how far it can go with moving the weight in an opposite way from the gradient ofthe mini-batch. When the learning rate is low, it can make training more reliable. The optimization process will need more time, but it is safe to say that training will be more reliable. If the learning rate is high, it will affect the training where it may not be possible to converge. Recommendations are that the model should start with a high learning rate so it can decrease during the training process and enable fine-tuning of the weight updates in the future.

The learning rate is a configurable hyperparameter, and it should be a priority during the training process. An example of learning rate formula, Deep Neural Networks is trained with the use of an optimization algorithm called Stochastic Gradient Descent. This algorithm estimates the error gradient for the current model state with examples from training data and updates the model weights with a backpropagation (as iterrors algorithm). Learning rate is the weight amount updated during the training. The most common values are between 0 and 1.

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