<prompt> Bolela mokhoa o le mong oa ho eketsa lintlha le hore na o sebetsa joang.nan  <response>: Instruction in English: Name one data augmentation technique and how it works. . Response in English: One common data augmentation technique is flipping images. This technique creates new training data by flipping existing images in the training set horizontally, vertically or both.For example, if we have an image of a cat facing left, the augmented image will show the cat facing right. This helps the model learn to recognize the object in different orientations and thus reduce overfitting.This technique works by creating mirrored versions of the original images in the data set. The flipping can be performed using image processing libraries such as OpenCV, PIL, or TensorFlow. These libraries have built-in functions to flip images horizontally or vertically.Overall, flipping images is a simple yet effective data augmentation technique to increase the amount of data available to train a model, and improve its performance. . Response in Sesotho: Mokhoa o mong o tloaelehileng oa ho eketsa lintlha ke ho phetla litšoantšo. Mokhoa ona o theha lintlha tse ncha tsa koetliso ka ho phetla litšoantšo tse teng setsing sa koetliso ka mokhoa o otlolohileng, o otlolohileng kapa ka bobeli. Ka mohlala, haeba re e-na le setšoantšo sa katse e shebileng ka ho le letšehali, setšoantšo se ekelitsoeng se tla bontša katse e shebileng ka ho le letona. Sena se thusa mohlala ho ithuta ho lemoha ntho ka mekhoa e fapaneng me kahoo e fokotsa ho feta. Mokhoa ona o sebetsa ka ho theha mefuta e bontsitsoeng ea litšoantšo tsa mantlha setsing sa data. Ho phethola ho ka etsoa ho sebelisoa lilaebrari tsa tsebetso ea litšoantšo tse kang OpenCV, PIL, kapa TensorFlow. Lilaebrari tsena li na le mesebetsi e hahelletsoeng ka hare ho phetla litšoantšo ka holimo kapa ka holimo. Ka kakaretso, ho phetla litšoantšo ke mokhoa o bonolo empa o sebetsa oa ho eketsa lintlha ho eketsa palo ea lintlha tse fumanehang ho koetlisa mohlala, le ho ntlafatsa tsebetso ea ona. 