<prompt> Hlahisa mokhoa oa ho thibela ho fetella ha mokhoa o tebileng oa ho ithuta.nan  <response>: Instruction in English: Suggest a way to preventing the overfitting of a deep learning model. . Response in English: One way to prevent overfitting in a deep learning model is to use a technique called dropout. Dropout is a form of regularization that involves randomly dropping out or setting to zero some of the hidden units during the training process. This reduces the co-dependency of hidden units and encourages the model to learn a more robust representation of the data. The dropout rate, which is the fraction of the hidden units to drop out, is a tunable hyperparameter and can be adjusted to fit the specific needs of the model. Another way to prevent overfitting is to use early stopping, where training is halted when the performance on a validation dataset stops improving. Additionally, using techniques such as cross-validation, L1 or L2 regularization, and data augmentation can also help prevent overfitting in a deep learning model. . Response in Sesotho: Tsela e ngoe ea ho thibela ho fetella ka mokhoa o tebileng oa ho ithuta ke ho sebelisa mokhoa o bitsoang dropout. Dropout ke mokhoa oa ho itloaetsa o kenyelletsang ho tlohela kapa ho beha likarolo tse ling tse patehileng nakong ea thupelo. Sena se fokotsa ho itsepaha ha likarolo tse patiloeng mme se khothaletsa mohlala ho ithuta boemeli bo matla haholoanyane ba data. Sekhahla sa ho tlohela sekolo, e leng karoloana ea likarolo tse patiloeng tse lokelang ho tloheloa, ke hyperparameter e ka fetoloang hore e lumellane le litlhoko tse khethehileng tsa mohlala. Tsela e ngoe ea ho thibela ho feta tekano ke ho sebelisa ho emisa kapele, moo koetliso e emisoang ha tsebetso ea dataset ea netefatso e emisa ho ntlafala. Ntle le moo, ho sebelisa mekhoa e joalo ka netefatso ea sefapano, L1 kapa L2 khafetsa, le keketseho ea data le tsona li ka thusa ho thibela ho fetella ka mokhoa o tebileng oa ho ithuta. 