<prompt> Bapisa le ho bapisa marang-rang a convolutional neural le marang-rang a tloaelehileng a neural.nan  <response>: Instruction in English: Compare and contrast convolutional neural networks with recurrent neural networks. . Response in English: Convolutional neural networks CNNs and recurrent neural networks RNNs are two common types of neural networks, but they differ in their architecture, operations, and applications.Architecture:CNNs typically have a layered architecture composed of convolutional, pooling, and fully connected layers, often used for image classification and object recognition. The layers are arranged in a feedforward manner, meaning the information flows from the input layer to the output layer without loops or backward connections. CNNs use filters that are applied to local sections of an input in a sliding window manner.On the other hand, RNNs have a recurrent architecture that includes feedback connections, allowing information to loop back within the network. This enables RNNs to maintain an internal state or memory, making them suitable for sequential data. Operations:In CNNs, the convolution operation is a key component, which involves dot products between input data and learnable filter weights. The outputs of these dot products are summarized in feature maps that represent particular features of the input data. Pooling operations help reduce spatial dimensions while retaining relevant information.RNNs, on the other hand, use an update mechanism that updates the hidden state based on the new input and the previous hidden state. The update mechanism typically involves a combination of matrix operations and activation functions.Applications:Due to the differences in architecture and operations, CNNs and RNNs are often applied to different types of tasks. CNNs are commonly used in computer vision tasks, such as image classification, object detection, and segmentation. RNNs, on the other hand, are more frequently applied to natural language processing tasks, such as text generation, language translation, and sentiment analysis, as well as time series prediction.Overall, CNNs and RNNs are designed to tackle different types of data and tasks, and while they share some commonalities, they have fundamental differences in their architecture, operations, and applications. . Response in Sesotho: Convolutional neural networks CNNs le recurrent neural networks RNNs ke mefuta e meli e tloaelehileng ea marang-rang a neural, empa a fapana ka meaho, tsebetso le tsebeliso ea ona. Meaho: Li-CNN hangata li na le meaho e nang le meaho e entsoeng ka mekhahlelo e kopaneng, e kopanyang, le e hokahaneng ka botlalo, e sebelisoang hangata bakeng sa ho hlophisa litšoantšo le ho lemoha lintho. Likarolo li hlophisitsoe ka mokhoa oa phepelo, ho bolelang hore tlhahisoleseling e phalla ho tloha ho lera la ho kenya ho ea ho lera la tlhahiso ntle le liloko kapa likhokahano tse ka morao. Li-CNN li sebelisa li-filters tse sebelisoang likarolong tsa sebaka sa hau tsa tlhahiso ka mokhoa o thellang oa fensetere. Ka lehlakoreng le leng, li-RNN li na le meralo e tloaelehileng e kenyelletsang likhokahano tsa maikutlo, tse lumellang tlhahisoleseling ho khutlela marang-rang. Sena se nolofalletsa li-RNN ho boloka boemo ba ka hare kapa mohopolo, ho li etsa hore li tšoanelehe bakeng sa data e latellanang. Tsebetso: Ho li-CNNs, tsebetso ea convolution ke karolo ea bohlokoa, e kenyelletsang lihlahisoa tsa matheba lipakeng tsa data e kentsoeng le boima bo ka ithutoang ba sefa. Liphetho tsa lihlahisoa tsena tsa matheba li akaretsoa ho limmapa tsa likarolo tse emelang likarolo tse itseng tsa data e kentsoeng. Tsebetso ea ho kopanya e thusa ho fokotsa boholo ba sebaka ha o ntse o boloka tlhahisoleseling e nepahetseng. Ka lehlakoreng le leng, li-RNN li sebelisa mokhoa oa ho ntlafatsa o ntlafatsang boemo bo patiloeng ho latela tlhahiso e ncha le boemo bo fetileng bo patiloeng. Mokhoa oa ntlafatso hangata o kenyelletsa tsebetso ea matrix le tsebetso ea ho kenya tšebetsong. Likopo: Ka lebaka la liphapang tsa meralo le tsebetso, CNNs le RNNs hangata li sebelisoa mefuteng e fapaneng ea mesebetsi. Li-CNN li sebelisoa hangata mesebetsing ea pono ea komporo, joalo ka ho arola litšoantšo, ho lemoha lintho, le ho arola. Ka lehlakoreng le leng, li-RNN li sebelisoa khafetsa mesebetsing ea puo ea tlhaho, joalo ka tlhahiso ea mongolo, phetolelo ea puo, tlhahlobo ea maikutlo, hammoho le ponelopele ea letoto la nako. Ka kakaretso, li-CNN le li-RNN li etselitsoe ho sebetsana le mefuta e fapaneng ea data le mesebetsi, me ha li ntse li arolelana lintho tse tšoanang, li na le liphapang tsa mantlha meahong ea tsona, tsebetsong le litsebetsong. 