From the course: Deep Learning with Python: Optimizing Deep Learning Models

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Learning rate scheduling

Learning rate scheduling - Python Tutorial

From the course: Deep Learning with Python: Optimizing Deep Learning Models

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Learning rate scheduling

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- [Instructor] Learning rate scheduling is a technique used in deep learning to adjust the learning rate during the training process to improve convergence and model performance. The learning rate is one of the most important settings because it determines how the model adjust its internal parameters, weights and biases, and response to errors. If the learning rate is too high, the model might skip over the best solution and never converge. If it's too low, training becomes slow and might get stuck in less optimal solutions. Learning rate scheduling solves this by automatically adjusting the learning rate over time to make training faster and more effective. At the beginning of training, models often benefit from a higher learning rate 'cause it allows for large quick adjustments that help the model move toward a good general area of the solution. As training progresses, the learning rate is gradually lowered, so the model can fine tune its parameters with smaller adjustments…

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