optuna

Using Optuna for Hyperparameter Tuning in PyTorch

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Deep learning models are notoriously sensitive to hyperparameter choices. Learning rates, batch sizes, network architectures, dropout rates—these decisions dramatically impact model performance, yet finding optimal values through manual experimentation is time-consuming and inefficient. Optuna brings sophisticated hyperparameter optimization to PyTorch workflows through an elegant API that supports advanced search strategies, pruning of unpromising trials, and … Read more

Hyperparameter Tuning with Optuna vs Ray Tune

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Hyperparameter tuning remains one of the most critical yet time-consuming aspects of machine learning model development. As models become more complex and datasets grow larger, the choice of optimization framework can significantly impact both the quality of results and the efficiency of the tuning process. Two leading frameworks have emerged as popular choices among data … Read more