<prompt> Hlalosa hore na mohopolo oa sekhahla sa ho ithuta o sebelisoa joang thutong ea mochini.nan  <response>: Instruction in English: Explain how the concept of learning rate is used in machine learning. . Response in English: In machine learning, the learning rate is a hyperparameter that determines the step size taken by an optimization algorithm during iterative training in order to minimize the loss function. The learning rate plays an important role in controlling how quickly or slowly a neural network model learns from the training data.When the learning rate is set too high, the model tends to overshoot the minimum of the loss function, and may even diverge, meaning that the model will fail to learn from the training data. On the other extreme, when the learning rate is set too low, the optimization process takes smaller steps towards the minimum of the loss function, leading to slower convergence and longer training times. A learning rate that is just right will allow the optimization algorithm to quickly converge to the minimum of the loss function, resulting in a model that can accurately learn from the training data. There are several methods to choose the right learning rate, including learning rate schedules that allow the learning rate to vary during training or using adaptive learning rate algorithms, such as Adagrad or Adam, which automatically adjust the learning rate during training based on the gradient information.In summary, the learning rate is a critical hyperparameter in machine learning that controls the rate at which a model learns from the training data. Choosing the right learning rate can lead to faster convergence and better performance of a machine learning model. . Response in Sesotho: Thutong ea mochini, sekhahla sa ho ithuta ke hyperparameter e lekanyetsang boholo ba mohato o nkuoeng ke algorithm ea optimization nakong ea koetliso e pheta-phetoang molemong oa ho fokotsa tsebetso ea tahlehelo. Sekhahla sa ho ithuta se phetha karolo ea bohlokoa ho laola hore na mohlala oa neural network o ithuta kapele kapa butle hakae ho data ea koetliso. Ha tekanyo ea ho ithuta e behiloe holimo haholo, mohlala o atisa ho feta bonyane ba mosebetsi oa tahlehelo, me o ka ba oa fapana, ho bolelang hore mohlala o tla hlōleha ho ithuta ho tloha boitsebisong ba koetliso. Ka lehlakoreng le leng, ha sekhahla sa ho ithuta se behiloe tlase haholo, tsebetso ea ho ntlafatsa e nka mehato e menyenyane ho isa bonyane ba tsebetso ea tahlehelo, e lebisang ho khokahanyo butle le linako tse telele tsa koetliso. Sekhahla sa ho ithuta se nepahetseng se tla lumella algorithm ea optimization hore e fetohe ka potlako ho bonyane ba mosebetsi oa tahlehelo, e leng se hlahisang mohlala o ka ithutang ka nepo ho data ea koetliso. Ho na le mekhoa e maloa ea ho khetha sekhahla se nepahetseng sa ho ithuta, ho kenyeletsoa kemiso ea sekhahla sa ho ithuta e lumellang sekhahla sa ho ithuta ho fapana nakong ea koetliso kapa ho sebelisa li-algorithms tsa sekhahla sa ho ithuta, joalo ka Adagrad kapa Adam, e fetolang sekhahla sa ho ithuta ka bo eona nakong ea thupelo ho ipapisitsoe le tlhaiso-leseling ea gradient. . Ka kakaretso, sekhahla sa ho ithuta ke hyperparameter ea bohlokoa thutong ea mochini e laolang sekhahla seo mohlala o ithutang sona ho data ea koetliso. Ho khetha sekhahla se nepahetseng sa ho ithuta ho ka lebisa ho khokahanyo e potlakileng le tsebetso e ntle ea mohlala oa ho ithuta oa mochini. 