<prompt> Hlalosa kamoo neural network e sebetsang kateng.nan  <response>: Instruction in English: Describe how a neural network works. . Response in English: A neural network is a computational system designed to analyze and identify patterns within data. The basic structure of a neural network consists of layers of connected nodes, each representing an artificial neuron that can transmit a signal from one layer to another. The first layer is known as the input layer, where the initial data is fed in. The middle layers are known as hidden layers, and the last layer is called the output layer, where the final result is produced.Each neuron receives input from the neurons in the previous layer, multiplies them by specific weights, and sums them up. This sum is then passed through an activation function, that determines whether the neuron should fire and send a signal to the neurons in the next layer. The output from the final layer represents the result of the networks analysis of the input data.The neural network is trained by adjusting the weights of the connections between neurons using an algorithm known as backpropagation. The network is shown a series of training examples, and for each example, the network produces an output. This output is then compared to the desired output for that example, and the difference between the two is used to calculate an error. This error is then propagated backwards through the network, and the weights between neurons are updated to reduce the error. This process is repeated until the network produces an output that is accurate enough for the task at hand.Once trained, the neural network can be used to analyze new data and produce an output. This output can be a prediction, classification, or decision, depending on the task the network was designed to perform. . Response in Sesotho: Neural network ke mokhoa oa computational o etselitsoeng ho sekaseka le ho khetholla mekhoa ka hara data. Sebopeho sa motheo sa marang-rang a methapo ea kutlo se na le lihlopha tsa li-node tse hokahaneng, e ngoe le e ngoe e emela neuron ea maiketsetso e ka fetisang letšoao ho tloha leleng le leng ho ea ho le leng. Lera la pele le tsejoa e le lera la ho kenya, moo data ea pele e feptjoang teng. Likarolo tse bohareng li tsejoa e le lihlopha tse patehileng, me karolo ea ho qetela e bitsoa lera la tlhahiso, moo sephetho sa ho qetela se hlahisoang. Neuron e ngoe le e ngoe e fumana lintlha ho tsoa ho li-neuron tse karolong e fetileng, e li atise ka boima bo itseng, ebe e li akaretsa. Kakaretso ena e fetisoa ka tsebetso ea tsebetso, e etsang qeto ea hore na neuron e lokela ho chesa le ho romela letšoao ho li-neurone tse karolong e latelang. Sephetho se tsoang mothating oa ho qetela se emela sephetho sa tlhahlobo ea marang-rang ea data e kentsoeng. Neural network e koetlisoa ka ho lokisa boima ba likhokahano lipakeng tsa li-neurone ho sebelisa algorithm e tsejoang ka hore ke backpropagation. Marang-rang a bontšoa letoto la mehlala ea koetliso, me bakeng sa mohlala o mong le o mong, marang-rang a hlahisa tlhahiso. Sehlahisoa sena se bapisoa le sehlahisoa se lakatsehang bakeng sa mohlala oo, me phapang pakeng tsa tse peli e sebelisoa ho bala phoso. Phoso ena e fetisetsoa morao ka marang-rang, me litekanyo tse pakeng tsa li-neuron li nchafatsoa ho fokotsa phoso. Tsebetso ena e phetoa ho fihlela marang-rang a hlahisa tlhahiso e nepahetseng ka ho lekaneng bakeng sa mosebetsi o etsoang. Hang ha e se e koetlisitsoe, marang-rang a neural a ka sebelisoa ho hlahloba lintlha tse ncha le ho hlahisa tlhahiso. Sephetho sena e ka ba selelekela, sehlopha, kapa qeto, ho latela mosebetsi oo marang-rang a neng a etselitsoe ho o etsa. 