<prompt> Hlalosa hore na marang-rang a neural a ithuta joang.nan  <response>: Instruction in English: Explain how neural networks learn. . Response in English: Neural networks are a type of artificial intelligence model that is loosely based on the structure and function of the human brain. They consist of layers of interconnected nodes or neurons that process and transmit information. These networks are trained on large sets of data to recognize patterns and make predictions.The process of learning in a neural network involves adjusting the connections between the nodes, known as weights, to minimize the error in the networks predictions. This process is often done through an optimization algorithm such as gradient descent, which calculates the gradient of the loss function a measure of how wrong the networks prediction is with respect to the weights and adjust them accordingly. In supervised learning, the neural network is given a set of labeled training data - input-output pairs - and the goal is for the network to learn to produce the correct output for a given input. During each iteration of the training process, the network produces an output for a given input, and the error between this output and the desired output is measured. The weights of the connections between the nodes are then updated using backpropagation, an algorithm that computes the gradient of the error with respect to the weights and adjust them to minimize the error.Over time, through repeated iterations of this process, the network becomes better at producing the correct output for a given input. The network has essentially learned to recognize patterns in the input data and make predictions based on those patterns.In unsupervised learning, the neural network is given a set of unlabeled data, and the goal is for the network to learn underlying patterns or structure in the data. This type of learning is often used for tasks such as clustering or dimensionality reduction.Overall, neural networks learn by adjusting the weights of the connections between the nodes based on the error in their predictions, using optimization algorithms and techniques such as backpropagation. This allows them to recognize patterns and make predictions based on the data theyve been trained on. . Response in Sesotho: Neural network ke mofuta oa bohlale ba maiketsetso bo ipapisitseng le sebopeho le tšebetso ea boko ba motho. Li na le likarolo tsa li-node tse hokahaneng kapa li-neurone tse sebetsanang le ho fetisa tlhahisoleseling. Marang-rang ana a koetlisoa ka lihlopha tse kholo tsa data ho lemoha mekhoa le ho bolela esale pele. Mokhoa oa ho ithuta ka marang-rang a marang-rang o kenyelletsa ho lokisa likhokahano lipakeng tsa li-node, tse tsejoang e le litekanyo, ho fokotsa phoso e boletsoeng esale pele ea marang-rang. Tsebetso ena e atisa ho etsoa ka algorithm ea optimization e kang ho theoha ha gradient, e lekanyetsang sekhahla sa tsebetso ea tahlehelo tekanyo ea hore na ho lebelloa ha marang-rang ho fosahetse hakae mabapi le litekanyo le ho li lokisa ka nepo. Thutong e hlokometsoeng, marang-rang a neural a fuoa sete ea data ea koetliso e ngotsoeng - li-input-output pairs - me sepheo ke hore marang-rang a ithute ho hlahisa tlhahiso e nepahetseng bakeng sa tlhahiso e fanoeng. Nakong e ngoe le e ngoe ea tsebetso ea koetliso, marang-rang a hlahisa tlhahiso bakeng sa tlhahiso e fanoeng, me phoso pakeng tsa tlhahiso ena le tlhahiso e lakatsehang e lekanngoa. Boima ba likhokahano lipakeng tsa li-node ebe bo nchafatsoa ho sebelisoa li-backpropagation, algorithm e kopanyang sekhahla sa phoso mabapi le litekanyo le ho li lokisa ho fokotsa phoso. Ha nako e ntse e ea, ka ho pheta-pheta mokhoa ona, marang-rang a ba betere ho hlahisa tlhahiso e nepahetseng bakeng sa tlhahiso e fanoeng. Marang-rang a ithutile ho lemoha mekhoa ea boitsebiso le ho etsa likhakanyo ho latela mekhoa eo. Thutong e sa laoleheng, marang-rang a marang-rang a fuoa sete ea data e sa ngolisoang, me sepheo ke hore marang-rang a ithute mekhoa ea motheo kapa sebopeho sa data. Mofuta ona oa ho ithuta o atisa ho sebelisoa bakeng sa mesebetsi e kang ho kopanya kapa ho fokotsa boholo. Ka kakaretso, marang-rang a neural a ithuta ka ho lokisa boima ba likhokahano lipakeng tsa li-node ho ipapisitse le phoso e boletsoeng esale pele, a sebelisa li-algorithms le mahlale a tsoanang le tsebetso ea morao-rao. Sena se ba lumella ho lemoha mekhoa le ho bolela esale pele ho latela lintlha tseo ba ithutileng tsona. 