<prompt> Hlalosa lintlha tse 5 tsa bohlokoa tse lokelang ho nkoa ha ho etsoa mokhoa o tebileng oa ho ithuta.nan  <response>: Instruction in English: Identify 5 key parameters that should be considered when constructing a deep learning system. . Response in English: 1. Data: One of the most important aspects of building a deep learning system is having a large and clean data set. This data is used to train, validate and test the model. It is also important to ensure that the data is balanced, normalized and preprocessed appropriately to improve the models performance.2. Architecture: The architecture of the neural network is another important parameter, as it defines the structure and complexity of the model. The architecture includes the number of layers, the number of nodes or neurons, the type of layers and how the nodes are connected. The architecture should be designed in such a way that it can learn the relevant features from the data.3. Hyperparameters: Hyperparameters are variables that govern the entire training process, and their values are set before training begins. These include the learning rate, the batch size, the number of epochs, regularization, and optimization algorithms. The choice of hyperparameters can significantly impact the performance of a deep learning system.4. Loss Function: A deep learning system aims to minimize a loss function, which determines the difference between the predicted output and the actual output. The choice of loss function is thus critical, and it is dependent on the type of problem being solved.5. Hardware and Computing Resources: Deep learning models often require massive amounts of computational power for training. The hardware and computing resources available can influence the viability of the model, the time it takes to train, and the models resulting performance. The use of GPUs, TPUs, or cloud computing can speed up the training process. . Response in Sesotho: 1. Lintlha: E ngoe ea lintlha tsa bohlokoa ka ho fetisisa tsa ho aha mokhoa o tebileng oa ho ithuta ke ho ba le data e kholo le e hloekileng. Lintlha tsena li sebelisetsoa ho koetlisa, ho netefatsa le ho leka mohlala. Ho bohlokoa hape ho netefatsa hore data e leka-lekane, e tloaeleha ebile e hlophisitsoe esale pele ka nepo ho ntlafatsa tsebetso ea mohlala. 2. Mehaho ea Mehaho: Mehaho ea marang-rang a marang-rang ke parameter e ngoe ea bohlokoa, kaha e hlalosa sebopeho le ho rarahana ha mohlala. Mehaho e kenyelletsa palo ea lihlopha, palo ea li-node kapa li-neurone, mofuta oa lihlopha le hore na li-node li kopantsoe joang. Mehaho e lokela ho raloa ka tsela eo e ka ithutang likarolo tse amehang ho data. 3. Li-hyperparameters: Li-hyperparameters ke mefuta e fapaneng e laolang tsebetso eohle ea koetliso, me litekanyetso tsa tsona li behiloe pele koetliso e qala. Tsena li kenyelletsa sekhahla sa ho ithuta, boholo ba batch, palo ea linako, tloaelo, le li-algorithms tsa optimization. Khetho ea li-hyperparameter e ka ama tsebetso ea sistimi e tebileng ea ho ithuta. 4. Mosebetsi oa Tahlehelo: Mokhoa o tebileng oa ho ithuta o ikemiselitse ho fokotsa tsebetso ea tahlehelo, e khethollang phapang pakeng tsa tlhahiso e boletsoeng esale pele le tlhahiso ea sebele. Khetho ea tsebetso ea tahlehelo e bohlokoa ka hona, me e itšetlehile ka mofuta oa bothata bo rarolloang. 5. Lisebelisoa tsa Hardware le Computing: Mehlala ea ho ithuta e tebileng hangata e hloka matla a mangata a khomphutha bakeng sa koetliso. Lisebelisoa tsa Hardware le komporo tse fumanehang li ka ama tsebetso ea mohlala, nako eo e e nkang ho ikoetlisa, le tsebetso e hlahisoang ke mohlala. Tšebeliso ea li-GPU, TPU, kapa cloud computing e ka potlakisa mosebetsi oa koetliso. 