What is hyperparameter tuning in TensorFlow?
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What is hyperparameter tuning in TensorFlow?
Hyperparameter tuning is the process of optimizing the parameters that govern the training of a machine learning model in TensorFlow.
Why is it important to tune hyperparameters?
Tuning hyperparameters is crucial because it can significantly affect the model's performance, leading to better accuracy and generalization.
What are some common hyperparameters to tune?
Common hyperparameters include learning rate, batch size, number of epochs, and the architecture of the model itself.
How can I perform hyperparameter tuning in TensorFlow?
You can use tools like Keras Tuner or TensorFlow's built-in functions to automate the hyperparameter tuning process.
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