<prompt> Re lekanya katleho ea marang-rang a neural joang?nan  <response>: Instruction in English: How do we measure the success of a neural network? . Response in English: The success of a neural network is typically measured by its performance on a given task, such as prediction accuracy for a supervised learning task or the ability to generate realistic data in a generative task. This can be evaluated using various metrics such as accuracy, precision, recall, F1 score, or mean squared error, depending on the specific task and desired outcomes. Model performance is typically evaluated on a dataset separate from the training data, called a validation or test set, to ensure that the network can generalize to new, unseen data. Additionally, visual inspection of the results, as well as comparison to benchmarks or human performance, can provide further insight into the success of the model. . Response in Sesotho: Katleho ea marang-rang a methapo ea kutlo hangata e lekanyetsoa ka tsebetso ea eona mosebetsing o fanoeng, joalo ka ho nepahala ha ponelopele bakeng sa mosebetsi oa thuto o hlokometsoeng kapa bokhoni ba ho hlahisa data ea nnete mosebetsing oa tlhahiso. Sena se ka lekoa ho sebelisoa metrics e fapaneng joalo ka ho nepahala, ho nepahala, ho hopola, lintlha tsa F1, kapa phoso ea moelelo oa lisekoere, ho latela mosebetsi o itseng le liphetho tse lakatsehang. Tsebetso ea mohlala hangata e hlahlojoa ho dataset e arohaneng le data ea koetliso, e bitsoang netefatso kapa sete ea liteko, ho netefatsa hore marang-rang a ka akaretsa lintlha tse ncha, tse sa bonoeng. Ho phaella moo, tlhahlobo ea pono ea liphello, hammoho le ho bapisoa le litekanyetso kapa tsebetso ea batho, e ka fana ka temohisiso e eketsehileng ea katleho ea mohlala. 