<prompt> Hlalosa melemo ea ho sebelisa Convolutional Neural Network CNN.nan  <response>: Instruction in English: Explain the advantages the of using a Convolutional Neural Network CNN. . Response in English: A Convolutional Neural Network CNN is a type of Artificial Neural Network widely used for image and video recognition, among other applications. There are many advantages of using a CNN, some of which include:1. Natural handling of image data: CNNs are designed to take in input data in the form of images, unlike traditional Neural Networks which require the images to be flattened. This allows for the preservation of the spatial relationship between pixels, making them effective for image recognition tasks.2. Efficient parameter sharing: CNNs employ the concept of parameter sharing, where the same weights are used for multiple features within the same layer. This results in a dramatic reduction in the number of parameters to be learned, allowing for faster and more efficient training.3. Invariance to translation: CNNs automatically learn to be invariant to small transformations of the input, such as translations or rotations. This is useful in tasks such as object recognition, where the position of the object in the image should not affect the networks ability to recognize it.4. Hierarchical feature learning: CNNs learn multiple levels of representations, with each layer building upon the features detected by the previous one. This allows for hierarchical feature learning, where higher layers learn more abstract and complex patterns from the more primitive features extracted by earlier layers.In conclusion, CNNs offer several advantages for image recognition and other tasks in computer vision, such as natural handling of image data, efficient parameter sharing, translation invariance, and hierarchical feature learning. These advantages make them a powerful tool for image recognition and other computer vision tasks. . Response in Sesotho: Convolutional Neural Network CNN ke mofuta oa Artificial Neural Network e sebelisoang haholo ho lemoha litšoantšo le video, hara litsebetso tse ling. Ho na le melemo e mengata ea ho sebelisa CNN, e meng ea eona e kenyelletsa: 1. Tsebetso ea tlhaho ea data ea setšoantšo: Li-CNN li etselitsoe ho nka boitsebiso ba ho kenya ka mokhoa oa litšoantšo, ho fapana le Neural Networks ea setso e hlokang hore litšoantšo li batalatsoe. Sena se lumella ho boloka kamano ea sebaka pakeng tsa lipikselse, ho etsa hore li atlehe bakeng sa mesebetsi ea ho lemoha litšoantšo. 2. Ho arolelana li-parameter ka katleho: Li-CNN li sebelisa khopolo ea ho arolelana li-parameter, moo litekanyo tse tšoanang li sebelisetsoang likarolo tse ngata ka hara sekhahla se le seng. Sena se fella ka ho fokotseha ho hoholo ha palo ea litekanyetso tse lokelang ho ithutoa, ho lumella koetliso e potlakileng le e sebetsang haholoanyane. 3. Ho feto-fetoha ha phetolelo: Li-CNN li ithuta ka botsona ho feto-fetoha le liphetoho tse nyane tsa mantsoe, joalo ka liphetolelo kapa ho potoloha. Sena se na le thuso mesebetsing e kang ho lemoha ntho, moo boemo ba ntho e setšoantšong bo sa lokelang ho ama bokhoni ba marang-rang ho e lemoha. 4. Ho ithuta ka likarolo tsa maemo a holimo: Li-CNN li ithuta maemo a mangata a boemeli, me lera le leng le le leng le haha ka holima likarolo tse bonoeng ke e fetileng. Sena se lumella ho ithuta likarolo tsa maemo a holimo, moo likarolo tse phahameng li ithutang lipaterone tse hlakileng le tse rarahaneng ho tsoa ho likarolo tsa khale tse ntšitsoeng ke likarolo tsa pejana. Qetellong, li-CNN li fana ka melemo e mengata bakeng sa temoho ea litšoantšo le mesebetsi e meng ponong ea khomphutha, joalo ka ho sebetsana le data ea litšoantšo ka tlhaho, ho arolelana liparamente hantle, ho se feto-fetohe ha phetolelo, le ho ithuta likarolo tsa maemo a holimo. Melemo ena e ba etsa sesebelisoa se matla sa ho lemoha litšoantšo le mesebetsi e meng ea pono ea khomphutha. 