<prompt> Hlalosa bohlokoa ba sekhahla sa ho ithuta bakeng sa marang-rang a methapo ea kutlo.nan  <response>: Instruction in English: Explain what is the significance of a learning rate for a neural network. . Response in English: The learning rate is a hyper-parameter that determines how much a neural network updates its weights and biases with each iteration during training. In other words, it governs the step size that the model takes while descending down the gradient to minimize the loss function. Having an appropriate learning rate is essential for the effective and efficient training of a neural network. If the learning rate is too high, the models weights can be updated too dramatically, causing the model to miss the global minimum of the loss function and overshoot into a suboptimal solution. In contrast, if the learning rate is too low, the training process can become very slow, taking an unnecessarily long time to converge, or may even stagnate at a suboptimal solution.Finding the optimal learning rate is often achieved through trial and error, where the learning rate is gradually decreased until the loss function converges. Some more advanced optimization algorithms, such as adaptive gradient methods, can also automatically adjust the learning rate during training.In summary, the significance of the learning rate for a neural network is that it controls the rate at which the model learns from the data, affecting both the speed and the final quality of the training process. Getting the value right is crucial, and usually requires careful tuning of this important hyper-parameter. . Response in Sesotho: Sekhahla sa ho ithuta ke hyper-parameter e khethollang hore na marang-rang a neural a ntlafatsa boima ba eona le litšekamelo tse kae ka nako e ngoe le e ngoe nakong ea koetliso. Ka mantsoe a mang, e laola boholo ba mohato oo mohlala o o nkang ha o theoha ho theosa le gradient ho fokotsa tsebetso ea tahlehelo. Ho ba le sekhahla se loketseng sa ho ithuta ho bohlokoa bakeng sa koetliso e sebetsang le e sebetsang ea marang-rang a methapo ea kutlo. Haeba sekhahla sa ho ithuta se le holimo haholo, boima ba mohlala bo ka ntlafatsoa ka mokhoa o makatsang, ho etsa hore mohlala o hloloheloe bonyane ba tsebetso ea tahlehelo mme o fetele tharollong e nyane. Ka lehlakoreng le leng, haeba sekhahla sa ho ithuta se le tlase haholo, tsebetso ea koetliso e ka tsamaea butle haholo, ea nka nako e telele ho sa hlokahale hore e kopane, kapa e ka ba ea thekesela ka tharollo e fokolang. Ho fumana tekanyo e nepahetseng ea ho ithuta hangata ho finyelloa ka teko le phoso, moo sekhahla sa ho ithuta se fokotseha butle-butle ho fihlela tsebetso ea tahlehelo e kopana. Li-algorithms tse ling tse tsoetseng pele tsa ntlafatso, joalo ka mekhoa e feto-fetohang ea gradient, le tsona li ka fetola sekhahla sa ho ithuta nakong ea boikoetliso. Ka kakaretso, bohlokoa ba tekanyo ea ho ithuta bakeng sa marang-rang a marang-rang ke hore e laola lebelo leo mohlala o ithutang ka lona ho data, e amang lebelo le boleng ba ho qetela ba mokhoa oa koetliso. Ho fumana boleng bo nepahetseng ho bohlokoa, me hangata ho hloka tokiso e hlokolosi ea hyper-parameter ena ea bohlokoa. 