<prompt> Theha polelo ho pheha khang hore na ke hobaneng ha mekhoa ea ho eketsa lintlha e lokela ho sebelisoa bakeng sa thuto e tebileng.nan  <response>: Instruction in English: Construct a statement to argue why data augmentation techniques should be used for deep learning. . Response in English: Data augmentation techniques are essential for deep learning because they help to increase the amount and diversity of training data available to the model, leading to improved performance and generalization. By applying various transformations to existing data such as rotations, flipping, cropping, and zooming, these techniques allow the model to learn from different variations of the same image or data point, making it more robust to changes or noise in the input. Additionally, data augmentation helps to reduce overfitting, as the model is not exposed to the same images repeatedly and is forced to learn more general features. Overall, data augmentation techniques are a valuable tool in the deep learning arsenal, enabling models to achieve better results and perform more effectively on real-world data. . Response in Sesotho: Mekhoa ea ho eketsa lintlha e bohlokoa bakeng sa ho ithuta ka botebo hobane e thusa ho eketsa palo le mefuta-futa ea lintlha tsa koetliso tse fumanehang ho mohlala, e leng se lebisang ho ntlafatso ea tsebetso le kakaretso. Ka ho sebelisa liphetoho tse fapaneng ho data e teng joalo ka ho potoloha, ho phethola, ho lema, le ho atametsa, mekhoa ena e lumella mohlala ho ithuta ho tsoa mefuteng e fapaneng ea setšoantšo se le seng kapa ntlha ea data, e leng ho etsang hore e be matla haholoanyane liphetohong kapa lerata le kenang. Ho phaella moo, ho eketsa lintlha ho thusa ho fokotsa ho feta, kaha mohlala ha o pepese litšoantšo tse tšoanang khafetsa me o qobelloa ho ithuta likarolo tse ling tse akaretsang. Ka kakaretso, mekhoa ea ho eketsa lintlha ke sesebelisoa sa bohlokoa ho arsenal ea ho ithuta e tebileng, e nolofalletsang mehlala ho finyella liphello tse molemo le ho sebetsa ka katleho ho data ea sebele ea lefatše. 