লক্ষ্য করুন: এই এন্ট্রির অনুবাদ বর্তমানে মান পর্যালোচনার অধীনে রয়েছে, তাই কিছু বিষয়বস্তু সাময়িকভাবে শুধুমাত্র ইংরেজিতে প্রদর্শিত হচ্ছে।
এই এন্ট্রিটি এখনও আপনার ভাষায় অনুবাদ করা হয়নি, তাই নিচে মূল লেখাটি দেখানো হচ্ছে।
hyperparameter
This term is specific to the domain of machine learning and computational statistics. It describes the external settings that govern the training process, distinguishing them from parameters, which are the internal weights the model learns automatically from the data.
In a technical context, hyperparameters are often subject to "tuning" or "optimization," where a developer experiments with different values to find the most efficient configuration for a specific dataset. This process is distinct from the actual training phase of the model.
Meanings
Examples
We need to optimize the hyperparameter settings to improve the accuracy of the neural network.
Which hyperparameter should I tweak first to reduce the overfitting?
The learning rate is the most critical hyperparameter in this specific architecture.
I wonder if changing this hyperparameter will stabilize the training process.
Grid search is a common method for finding the best hyperparameter combination.
The model performance dropped significantly after he altered the hyperparameter values.
The model performance dropped significantly after he altered the hyperparameter values.
A poorly chosen hyperparameter can lead to a model that never converges.
Let us document every hyperparameter used in this experiment for reproducibility.
Let us document every hyperparameter used in this experiment for reproducibility.