<prompt> Hlalosa hore na mofuta o tloaelehileng oa marang-rang oa neural o sebelisoa joang.Multi-Layer Perceptron MLP  <response>: Instruction in English: Explain how a common type of neural network is used. . Response in English: A Multi-Layer Perceptron MLP is a type of artificial neural network that contains multiple layers of interconnected nodes, typically including one or more hidden layers. MLPs are often used for supervised learning tasks such as classification and regression.An MLP is typically composed of an input layer, one or more hidden layers, and an output layer. Each layer contains a number of nodes, also known as neurons, which are connected to the nodes in the previous and next layers. Each connection between neurons has a weight associated with it, which defines the strength of the connection.When an MLP is used for a classification or regression task, the input layer receives the input data, which is then processed by the hidden layers to extract relevant features. The extracted features are then used by the output layer to generate a prediction.The goal of training an MLP is to find a set of weights that minimizes the difference between the predicted outputs and the true outputs. This is typically achieved using a technique known as backpropagation, in which the error is propagated backwards through the network and the weights are adjusted accordingly. The training process continues until the error reaches an acceptable level or a maximum number of iterations is reached.Once trained, an MLP can be used to make predictions on new data by propagating the input data through the network and using the output of the final layer as the predicted output. . Response in Sesotho: Multi-Layer Perceptron MLP ke mofuta oa marang-rang a maiketsetso a maiketsetso a nang le likarolo tse ngata tsa li-node tse hokahaneng, tseo hangata li kenyelletsang karolo e le ngoe kapa tse ngata tse patiloeng. Li-MLP li atisa ho sebelisoa bakeng sa mesebetsi ea ho ithuta e hlokometsoeng e kang ho hlophisa le ho khutlela morao. Hangata MLP e entsoe ka lera le kenyang, lesela le le leng kapa ho feta le patiloeng, le lera la tlhahiso. Karolo e ngoe le e ngoe e na le li-node tse maloa, tse tsejoang hape e le li-neurone, tse hokahaneng le li-node tse karolong e fetileng le e latelang. Khokahano e ngoe le e ngoe lipakeng tsa li-neurone e na le boima bo amanang le eona, bo hlalosang matla a khokahano. Ha MLP e sebelisoa bakeng sa mosebetsi oa ho arola kapa oa ho khutlela morao, karolo ea ho kenya e fumana data ea ho kenya, ebe e sebetsoa ke lihlopha tse patiloeng ho ntša likarolo tse amehang. Likarolo tse nkiloeng li sebelisoa ke lera la tlhahiso ho hlahisa se boletsoeng esale pele. Sepheo sa ho koetlisa MLP ke ho fumana sete ea boima bo fokotsang phapang pakeng tsa liphello tse boletsoeng esale pele le liphello tsa nete. Hangata sena se finyelloa ho sebelisoa mokhoa o tsejoang e le backpropagation, moo phoso e phatlalatsoang morao ka marang-rang me litekanyo li lokisoa ka tsela e nepahetseng. Mokhoa oa koetliso o tsoela pele ho fihlela phoso e fihla boemong bo amohelehang kapa palo e kholo ea ho pheta-pheta e fihletsoe. Hang ha e se e koetlisitsoe, MLP e ka sebelisoa ho bolela esale pele ka lintlha tse ncha ka ho phatlalatsa lintlha tsa ho kenya ka marang-rang le ho sebelisa tlhahiso ea lera la ho qetela e le tlhahiso e boletsoeng esale pele. 