<prompt> Tsela e molemo ka ho fetisisa ea ho ntlafatsa ho nepahala ka mokhoa o tebileng oa ho ithuta ke ofe?nan  <response>: Instruction in English: What is the best way to improve accuracy in a deep learning model? . Response in English: There are several ways to improve accuracy in a deep learning model, including:1. Increase the size of the training data: The more training data available to the model, the better it will be in identifying patterns and relationships between the features and the target variable.2. Hyperparameter optimization: fine-tune the hyperparameters of the model to improve its performance. Some of the hyperparameters that can be tuned include the learning rate, the batch size, the number of layers, and the number of neurons in each layer.3. Regularization techniques: Regularization methods such as dropout, L1, and L2 regularization can help to control overfitting and improve the accuracy of the model.4. Use an ensemble of models: combining the predictions of several models can help to improve the accuracy of the overall prediction.5. Experiment with different network architectures: The choice of network architecture can have a significant impact on the accuracy of the model. Experiment with different architectures and see which one gives the best results.6. Data augmentation: Augmenting the data by adding more variations of the existing data can help the model to learn more robust representations of the data and improve its accuracy.7. Feature engineering: carefully selecting and engineering the features can help to improve the accuracy of the model.In summary, there is no one-size-fits-all solution to improving the accuracy of a deep learning model. The best approach is to experiment with different techniques and see what works best for the specific problem at hand. . Response in Sesotho: Ho na le litsela tse maloa tsa ho ntlafatsa ho nepahala ka mokhoa o tebileng oa ho ithuta, ho kenyelletsa: 1. Eketsa boholo ba lintlha tsa koetliso: Boitsebiso bo bongata ba koetliso bo fumanehang ho mohlala, ho tla ba molemo ho khetholla mekhoa le likamano pakeng tsa likarolo le sepheo. feto-fetoha. 2. Hyperparameter optimization: lokisa hantle li-hyperparameter tsa mohlala ho ntlafatsa tshebetso ea eona. Tse ling tsa li-hyperparameter tse ka hlophisoang li kenyelletsa sekhahla sa ho ithuta, boholo ba batch, palo ea lihlopha, le palo ea li-neurone sebakeng se seng le se seng. 3. Mekhoa ea ho laola kamehla: Mekhoa ea kamehla e kang ho tlohela mosebetsi, L1, le L2 e tloaelehileng e ka thusa ho laola ho feta tekano le ho ntlafatsa ho nepahala ha mohlala. 4. Sebelisa ensemble ea mehlala: ho kopanya likhakanyo tsa mehlala e mengata ho ka thusa ho ntlafatsa ho nepahala ha ponelopele ka kakaretso. 5. Teko ka mekhoa e fapaneng ea meralo ea marang-rang: Khetho ea meralo ea marang-rang e ka ba le tšusumetso e kholo ho nepahala ha mohlala. Leka ka meaho e fapaneng me u bone hore na ke efe e fanang ka litholoana tse ntle ka ho fetisisa. 6. Keketso ea lintlha: Ho eketsa lintlha ka ho eketsa mefuta e mengata ea lintlha tse teng ho ka thusa mohlala ho ithuta litlhaloso tse matla tsa data le ho ntlafatsa ho nepahala ha eona. 7. Boenjiniere ba likarolo: ho khetha ka hloko le boenjiniere ba likarolo ho ka thusa ho ntlafatsa ho nepahala ha mohlala. Ka kakaretso, ha ho na tharollo e lekanang le e ngoe ea ho ntlafatsa ho nepahala ha mohlala o tebileng oa ho ithuta. Mokhoa o motle ke ho leka mekhoa e fapaneng le ho bona hore na ke eng e sebetsang hantle bakeng sa bothata bo itseng bo teng. 