<prompt> Hlalosa hore na Neural Networks e ka sebelisoa joang ho lemoha mongolonan  <response>: Instruction in English: Describe how Neural Networks can be used to recognize handwriting . Response in English: Neural networks can be used to recognize handwriting by being trained using a large dataset of sample handwriting, usually written character-by-character. Each sample is rasterized and pre-processed to improve the effectiveness of the recognition process, using techniques like normalization or noise removal. This pre-processed data is then fed into the neural network, which processes it through a series of connected layers made up of multiple neurons.The first layer of the neural network is called the input layer, and it receives the pre-processed data, with each input neuron responsible for one pixels value of the input image. The input layer is then connected to one or more hidden layers, where the data is processed and evaluated to generate a set of probability scores. These scores represent the likelihood of each possible character being the correct interpretation of the input image. The final layer of the network is the output layer, where the probabilities are outputted after applying an activation function.During the training process, the network adjusts its internal weights and biases to minimize the error between the predicted and correct characters using backpropagation. This process repeats until the network reaches the desired level of accuracy in recognizing handwriting.After the training process, the neural network can then recognize handwriting by taking input, pre-processing it, and feeding it through the network to obtain the interpretation of the handwritten character with the highest probability. In this way, Neural Networks can effectively recognize handwriting with high accuracy. . Response in Sesotho: Marang-rang a Neural a ka sebelisoa ho lemoha mongolo ka ho koetlisoa ho sebelisoa pokello ea lintlha tse ngata tsa mohlala oa mongolo, hangata o ngoloang ho latela litlhaku. Sampole e ngoe le e ngoe e rasterized me e sebetsoa esale pele ho ntlafatsa katleho ea tsebetso ea kananelo, ho sebelisoa mekhoa e kang ho tloaeleha kapa ho tlosa lerata. Lintlha tsena tse entsoeng esale pele li kenngoa ho neural network, e sebetsang ka letoto la likarolo tse hokahaneng tse entsoeng ka li-neurone tse ngata. Karolo ea pele ea netweke ea methapo e bitsoa input layer, me e amohela data e sebetsitsoeng esale pele, me neuron ka ngoe e ikarabellang bakeng sa boleng ba pixel e le ngoe ea setšoantšo se kentsoeng. Ka mora moo, karolo ea ho kenya e kopanngoa le karolo e le ngoe kapa tse ling tse patiloeng, moo data e sebetsoang le ho hlahlojoa ho hlahisa lintlha tse ngata tse ka khonehang. Lintlha tsena li emela monyetla oa hore motho e mong le e mong a be le tlhaloso e nepahetseng ea setšoantšo se kentsoeng. Lera la ho qetela la marang-rang ke lesela la lihlahisoa, moo menyetla e hlahisoang ka mora ho sebelisa tsebetso ea tsebetso. Nakong ea tsebetso ea koetliso, marang-rang a fetola litekanyo tsa eona tsa ka hare le litšekamelo ho fokotsa phoso pakeng tsa litlhaku tse boletsoeng esale pele le tse nepahetseng ho sebelisa li-backpropagation. Tsebetso ena e pheta-pheta ho fihlela marang-rang a fihla boemong bo lakatsehang ba ho nepahala ho lemoha mongolo oa letsoho. Ka mora tsebetso ea koetliso, neural network e khona ho lemoha mongolo ka ho kenya letsoho, ho e lokisa esale pele, le ho e fepa ka marang-rang ho fumana tlhaloso ea litlhaku tse ngotsoeng ka letsoho ka mokhoa o phahameng ka ho fetisisa. Ka tsela ena, Neural Networks e khona ho lemoha mongolo ka mokhoa o nepahetseng haholo. 