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cs - Hyper-parameter

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1. How control hyper-parameter in AI

Hyperparameters are parameters in machine learning models that are set before training and affect the learning process and the final model’s performance. Examples of hyperparameters include the learning rate, the number of hidden layers in a neural network, and the number of trees in a random forest model.

Controlling hyperparameters in AI involves finding the optimal values for these hyperparameters that result in the best performance of the model. There are several techniques for hyperparameter tuning, including:

  1. Grid Search: This technique involves manually defining a grid of hyperparameter values and testing each combination of hyperparameters to find the best performing model.

  2. Random Search: This technique involves randomly sampling hyperparameter values within predefined ranges and testing each combination of hyperparameters to find the best performing model.

  3. Bayesian Optimization: This technique uses a probabilistic model to predict the performance of different hyperparameter values and selects the next hyperparameter values to test based on the predictions.

  4. Genetic Algorithms: This technique uses an evolutionary approach to find the best combination of hyperparameters, where the fittest combinations are selected for further mutation and breeding.

  5. Automated Machine Learning (AutoML): This is an automated approach to hyperparameter tuning that uses algorithms to search for the optimal combination of hyperparameters.

Overall, controlling hyperparameters in AI involves a combination of domain knowledge, trial and error, and algorithmic optimization techniques to find the best hyperparameters for the specific machine learning problem at hand.