<prompt> Bapisa melemo le mathata a ho sebelisa marang-rang a tloaelehileng a neural khahlanong le convolutional neural network.nan  <response>: Instruction in English: Compare the advantages and disadvantages of using a recurrent neural network versus a convolutional neural network. . Response in English: Recurrent neural networks RNNs and convolutional neural networks CNNs are two commonly used deep learning architectures that differ widely in their structures, use cases, as well as their advantages and disadvantages.1. Advantages of RNNs:- RNNs are designed to handle input data with temporal and sequential characteristics. They can remember previous inputs through the hidden layers, allowing for the incorporation of context and position information into the output.- RNNs are useful in natural language processing tasks such as machine translation, sentiment analysis, speech recognition and text generation.2. Disadvantages of RNNs:- RNNs can be prone to the vanishing gradient problem, in which the gradient becomes smaller and smaller as it is back-propagated through the network, making it hard to train longer sequences.- RNNs can also be computationally expensive due to their sequential nature, which makes them unsuitable for processing large input data.3. Advantages of CNNs:- CNNs are effective at detecting local patterns within the input data through the use of convolutional filters, making them suitable for tasks such as image recognition and classification.- CNNs can reduce the number of parameters and computations through the use of weight sharing and pooling, enabling faster training and processing of large input data.4. Disadvantages of CNNs:- CNNs may struggle to capture global or high-level relationships within the input data due to their focus on local patterns.- CNNs are mostly used for structured data, particularly grid-like data such as images, and may not be as effective in processing unstructured data such as text.In conclusion, RNNs and CNNs each have their respective strengths and weaknesses and are suitable for different types of tasks. RNNs are effective in processing sequential data and capturing context information, while CNNs excel at detecting local patterns and handling large inputs efficiently. It is important to consider the nature of the input data and the task at hand when choosing between the two architectures. . Response in Sesotho: Marang-rang a tloaelehileng a neural RNNs le convolutional neural networks CNNs ke mekhoa e meli ea ho ithuta e tebileng e fapaneng haholo ka meaho ea bona, linyeoe tsa tšebeliso, hammoho le melemo le mathata a tsona. 1. Melemo ea li-RNN: - Li-RNN li etselitsoe ho sebetsana le lintlha tsa ho kenya tse nang le litšobotsi tsa nakoana le tse latellanang. Ba ka hopola likenyelletso tse fetileng ka mekhahlelo e patiloeng, e lumellang ho kenyelletsoa ha moelelo le boemo ba tlhahisoleseling ho sephetho. - Li-RNN li na le thuso mesebetsing ea tsebetso ea puo ea tlhaho joalo ka phetolelo ea mochini, tlhahlobo ea maikutlo, temoho ea puo le tlhahiso ea mongolo. 2. Mefokolo ea li-RNN: - Li-RNN li ka ba le bothata ba bothata bo nyamelang ba gradient, moo gradient e fetohang e nyane le e nyane ha e ntse e phatlalatsoa ka marang-rang, ho etsa hore ho be thata ho koetlisa tatellano e telele. - Li-RNN le tsona li ka bitsa chelete e ngata ka lebaka la tatellano ea tsona, e leng se etsang hore li se ke tsa sebetsa hantle bakeng sa ho sebetsana le lintlha tse ngata tse kentsoeng. 3. Melemo ea li-CNN: - Li-CNN li sebetsa hantle ho lemoha mekhoa ea sebaka ka hara boitsebiso ba boitsebiso ka ho sebelisa li-convolutional filters, ho etsa hore li tšoanelehe bakeng sa mesebetsi e kang ho lemoha litšoantšo le ho arola. - Li-CNN li ka fokotsa palo ea litekanyetso le lipalo ka tšebeliso ea ho arolelana boima ba mele le ho kopanya, ho nolofalletsa koetliso e potlakileng le ho sebetsana le lintlha tse kholo tsa ho kenya. 4. Mefokolo ea li-CNN: - Li-CNN li ka na tsa thatafalloa ke ho hapa likamano tsa lefatse kapa tsa boemo bo holimo ka hara boitsebiso ba boitsebiso ka lebaka la ho tsepamisa maikutlo holima mekhoa ea lehae. - Hangata li-CNN li sebelisoa bakeng sa data e hlophisitsoeng, haholo data e kang grid joalo ka litšoantšo, me e kanna ea se sebetse hantle ho sebetsa data e sa hlophisoang joalo ka mongolo. Qetellong, li-RNN le li-CNN li na le matla le mefokolo ea tsona me li loketse mefuta e fapaneng ea mesebetsi. Li-RNN li sebetsa hantle tabeng ea ho sebetsana le lintlha tse latellanang le ho hapa boitsebiso ba litaba, ha li-CNN li sebetsa hantle tabeng ea ho lemoha mekhoa ea sebaka sa heno le ho sebetsana le lintho tse ngata ka katleho. Ho bohlokoa ho nahana ka mofuta oa data e kentsoeng le mosebetsi o teng ha u khetha lipakeng tsa meralo e meli. 