<prompt> Thathamisa mekhoa e meraro ea ho ntlafatsa tsebetso ea mohlala o tebileng oa ho ithutanan  <response>: Instruction in English: List three methods for improving the performance of a deep learning model . Response in English: 1. Incorporate more data: Deep learning algorithms crave data and their accuracy potential improves with larger datasets. If the model is suffering from the bias problem underfitting, adding more data will allow the model to get exposed to the many variances present in data and help it perform better.2. Adjusting the network architecture: The structure of the neural network can be adjusted to enhance its performance, via addingremoving layers, altering the number of neurons per layer, addingremoving neural network components, such as dropouts, batch normalization, or changing activation functions. Experimentation with various architectures and discovering the optimum architecture is key to achieve the desired performance.3. Tuning Hyperparameters: Hyperparameters are the external configurable parameters of the model which cant be learned through training. Learning rate, batch size, number of epochs, and regularization parameters are among the most important hyperparameters which should be meticulously tuned to achieve the best performance. Grid search, random search, and Bayesian optimization are popular approaches for tuning hyperparameters. . Response in Sesotho: 1. Kenyelletsa lintlha tse ling: Litaelo tsa ho ithuta ka botebo li labalabela boitsebiso me bokhoni ba tsona ba ho nepahala boa ntlafala ka li-database tse kholoanyane. Haeba mohlala o na le bothata ba leeme underfitting, ho eketsa lintlha tse ngata ho tla lumella mohlala ho pepeseha ho mefuta e mengata e teng ho data le ho o thusa ho sebetsa hantle. 2. Ho fetola meralo ea marang-rang : Sebopeho sa marang-rang sa marang-rang se ka fetoloa ho ntlafatsa tsebetso ea sona, ka ho eketsa ho tlosa lihlopha, ho fetola palo ea li-neuron ka lera, ho eketsa ho tlosa likarolo tsa neural network, tse kang ho tlohela mosebetsi, batch normalization, kapa ho fetola tsebetso ea tsebetso. Ho etsa liteko ka meaho e fapaneng le ho fumana meaho e nepahetseng ke senotlolo sa ho fihlela tsebetso e lakatsehang. 3. Tuning Hyperparameters: Li-hyperparameter ke li-parameter tse ka lokisoang ka ntle tsa mohlala tse ke keng tsa ithutoa ka koetliso. Sekhahla sa ho ithuta, boholo ba batch, palo ea linako, le maemo a tloaelehileng ke tse ling tsa li-hyperparameter tsa bohlokoa tse lokelang ho hlophisoa ka hloko ho fihlela tšebetso e ntle ka ho fetisisa. Patlo ea grid, patlo e sa reroang, le ntlafatso ea Bayesian ke mekhoa e tsebahalang ea ho lokisa li-hyperparameter. 