Description
The learning rate is a Hyperparameter in machine learning that determines the step size at which a model's parameters are updated during training. It plays a significant role in the optimization process, particularly in algorithms like Gradient Descent which are used to minimize the Loss function.
Key Points about Learning Rate:
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Parameter Updates:
- During training, the model’s parameters (such as weights and biases in neural networks) are adjusted iteratively to minimize the loss function.
- The learning rate controls how much the parameters are changed in response to the estimated error each time the model weights are updated.
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Impact on Training/ Convergence
- A high learning rate can lead to faster convergence but risks overshooting the optimal solution, potentially causing the model to diverge.
- A low learning rate ensures more stable and precise convergence but may result in slow training and can get stuck in local minima. A lower learning rate makes the model more robust but requires more iterations to converge.
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Tuning:
- The learning rate is a hyperparameter that needs careful tuning. It can be adjusted manually or through automated hyperparameter optimization techniques like Optuna.
- The optimal learning rate depends on various factors, including the dataset, model complexity, and the specific optimization algorithm used.
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Practical Considerations:
- It’s common to start with a moderate learning rate and adjust based on the model’s performance during training.
- Techniques like learning rate schedules or adaptive learning rate methods (e.g., Adam Optimizer) can dynamically adjust the learning rate during training to improve convergence.
This impacts the efficiency of Gradient Descent
Effects occur if too small (takes long), or too large (over shoots missing the minima).
What happens if you are at a local minima? Then no change.
