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Choose the Optimal Learning Rate with Python
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Learning rate in neural networks is a hyperparameter that controls the step size at which the model's parameters are updated during training.
A high learning rate means that the model's parameters will be adjusted quickly in response to errors during training, but this can lead to overshooting the optimal solution and instability.
A low learning rate means that the model's parameters will be adjusted more slowly, which may result in slower convergence to an optimal solution, but it can also make the training process more stable.
I demonstrated the impact of different learning rates to the training results and how to implement the model in Python.
You are welcome to provide your comments and subscribe to my YouTube channel.
A high learning rate means that the model's parameters will be adjusted quickly in response to errors during training, but this can lead to overshooting the optimal solution and instability.
A low learning rate means that the model's parameters will be adjusted more slowly, which may result in slower convergence to an optimal solution, but it can also make the training process more stable.
I demonstrated the impact of different learning rates to the training results and how to implement the model in Python.
You are welcome to provide your comments and subscribe to my YouTube channel.
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