<prompt> Hlalosa sepheo sa ho theha mohlala oa tatellano thutong e tebileng.nan  <response>: Instruction in English: Explain the purpose of creating a sequential model in deep learning. . Response in English: In deep learning, Sequential model is a linear stack of layers, where each layer has exactly one input tensor and one output tensor. This type of model architecture is often used to build artificial neural networks for a variety of tasks including classification, regression, and prediction.The purpose of creating a sequential model in deep learning is to simplify the process of building and training a neural network. By arranging the layers in a linear fashion, the model can be easily constructed by simply specifying the input and output dimensions of each layer, and the type of activation function to use.The model can then be trained by feeding it input data and corresponding output labels, and using an optimization algorithm to adjust the weights of the connections between the layers in order to minimize the loss function. This process can be iterated until the model converges to a set of weights that produce accurate predictions for the given task.Sequential models are particularly well-suited for data with temporal or spatial structure, such as time-series data, speech signals, text, and images. By stacking layers of varying complexity, the model is able to learn a hierarchy of features, where lower-level features are combined to form higher-level representations.In summary, the purpose of creating a sequential model in deep learning is to simplify the process of building and training a neural network, by arranging the layers in a linear stack and training the model to learn a hierarchy of features that accurately represent the given data. . Response in Sesotho: Thutong e tebileng, mohlala oa Sequential ke letoto la mekhahlelo, moo karolo e ngoe le e ngoe e nang le tensor e le ngoe e kenang hantle le tensor e le ngoe ea tlhahiso. Mofuta ona oa meralo ea meralo hangata o sebelisoa ho aha marang-rang a maiketsetso a methapo ea kutlo bakeng sa mesebetsi e fapaneng e kenyelletsang ho hlopha, ho theola maemo le ho bolela esale pele. Sepheo sa ho theha mohlala o latellanang thutong e tebileng ke ho nolofatsa mokhoa oa ho haha le ho koetlisa marang-rang a methapo. Ka ho hlophisa lihlopha ka mokhoa o tsoanang, mohlala o ka hahuoa habonolo feela ka ho hlakisa litekanyo tsa ho kenya le ho hlahisa karolo ka ngoe, le mofuta oa tsebetso e lokelang ho sebelisoa. Joale mohlala o ka koetlisoa ka ho o fepa data ea ho kenya le li-labels tse tsamaellanang tsa tlhahiso, le ho sebelisa algorithm ea optimization ho lokisa boima ba likhokahano lipakeng tsa mekhahlelo e le ho fokotsa tsebetso ea tahlehelo. Tshebetso ena e ka phetoa ho fihlela mohlala o kopana ho sehlopha sa boima bo hlahisang likhakanyo tse nepahetseng bakeng sa mosebetsi o fanoeng. Mefuta e latellanang e loketse haholo data e nang le sebopeho sa nakoana kapa sa sebaka, joalo ka data ea nako, matshwao a puo, mongolo le ditshwantsho. Ka ho bokella likarolo tsa ho rarahana ho fapaneng, mohlala o khona ho ithuta lethathamo la likarolo, moo likarolo tsa boemo bo tlase li kopantsoeng ho etsa lipontšo tsa boemo bo holimo. Ka kakaretso, sepheo sa ho theha mohlala o latellanang thutong e tebileng ke ho nolofatsa mokhoa oa ho haha le ho koetlisa marang-rang a neural, ka ho hlophisa likarolo ka hara stack e nang le linear le ho koetlisa mohlala ho ithuta likarolo tsa likarolo tse emelang data e fanoeng ka nepo. . 