<prompt> Hlalosa hore na neural network e ka sebelisoa joang ho lemoha mantsoe a ngotsoeng ka letsoho.nan  <response>: Instruction in English: Explain how a neural network can be used to recognize handwritten words. . Response in English: A neural network can be used to recognize handwritten words by training it on a large dataset of examples, where the input is an image of a handwritten word and the output is the corresponding text. The network is composed of several layers of interconnected artificial neurons that learn to recognize patterns in the data by adjusting their connections through an iterative process.During the training process, the network analyzes each image and predicts the text of the handwritten word. With each prediction, the computer calculates the error or the difference between the predicted value and the actual value this is known as the cost or loss function. The goal of training is to minimize the cost and improve the predictive power of the neural network.One method of minimizing the cost is by using backpropagation, where the error is propagated backwards through the network and the connection weights between neurons are adjusted to reduce the error. This process is repeated for many iterations until the network can correctly classify the handwritten words with high accuracy.Once the network is adequately trained, it can be used to recognize new handwritten words by passing the image through the network and interpreting the output as the predicted text. By using such neural network architectures, the computer can identify complex patterns and nuances in handwriting, making it capable of recognizing and transcribing handwritten words accurately. . Response in Sesotho: Neural network e ka sebelisoa ho lemoha mantsoe a ngotsoeng ka letsoho ka ho a koetlisa ho dataset e kholo ea mehlala, moo tlhahiso e leng setšoantšo sa lentsoe le ngotsoeng ka letsoho me tlhahiso e le mongolo o lumellanang. Marang-rang a entsoe ka likarolo tse maloa tsa li-neurone tsa maiketsetso tse hokahaneng tse ithutang ho lemoha lipaterone ho data ka ho lokisa likhokahano tsa tsona ka tsebetso e pheta-phetoang. Nakong ea thupelo, marang-rang a hlahloba setšoantšo se seng le se seng me a bolela esale pele mongolo oa lentsoe le ngotsoeng ka letsoho. Ka polelo e ngoe le e ngoe, khomphieutha e bala phoso kapa phapang pakeng tsa boleng bo boletsoeng esale pele le boleng ba sebele - sena se tsejoa e le mosebetsi oa litšenyehelo kapa tahlehelo. Sepheo sa koetliso ke ho fokotsa litšenyehelo le ho ntlafatsa matla a ho bolela esale pele a neural network. Mokhoa o mong oa ho fokotsa litšenyehelo ke ka ho sebelisa li-backpropagation, moo phoso e phatlalatsoang morao ka marang-rang me boima ba ho hokahanya pakeng tsa li-neurons bo lokisoa ho fokotsa phoso. Tsebetso ena e phetoa ka makhetlo a mangata ho fihlela marang-rang a khona ho arola ka nepo mantsoe a ngotsoeng ka letsoho ka ho nepahala ho phahameng. Hang ha marang-rang a koetlisitsoe ka ho lekaneng, a ka sebelisoa ho lemoha mantsoe a macha a ngotsoeng ka letsoho ka ho fetisa setšoantšo ka marang-rang le ho hlalosa tlhahiso e le mongolo o boletsoeng esale pele. Ka ho sebelisa meralo e joalo ea marang-rang a marang-rang, khomphieutha e khona ho khetholla mekhoa e rarahaneng le likaroloana tse ngotsoeng ka letsoho, e leng se etsang hore e khone ho lemoha le ho ngola mantsoe a ngotsoeng ka letsoho ka nepo. 