<prompt> Bapisa molemo le bobe ba ho sebelisa mohlala oa neural network RNN.nan  <response>: Instruction in English: Compare the advantage and disadvantage of using a recurrent neural network RNN model. . Response in English: A recurrent neural network RNN is a type of neural network that is particularly effective in processing sequential data because it has the ability to retain information from the past and use it to make decisions in the present. Here are some advantages and disadvantages of using an RNN model:Advantages:- One of the biggest advantages of using an RNN is their ability to handle sequential data of variable length, which makes them well-suited for tasks such as speech recognition, language translation, and sentiment analysis.- RNNs can also learn contextual relationships between elements in a sequence, allowing them to make predictions about future events based on past observations.- Due to their ability to process information over time, RNNs are capable of detecting patterns in data that might be difficult for other types of models to identify.- RNNs can also be used for generating new sequences of data, which can be useful in tasks such as text generation or music composition.Disadvantages:- One disadvantage of using an RNN is the vanishing gradient problem, which arises when the influence of earlier events in the sequence decreases exponentially over time. This can make it difficult for the model to retain information that is important to the task at hand.- Another challenge with using RNNs is that they can be computationally expensive and time-consuming to train, particularly when processing long sequences of data.- RNNs may also struggle with handling dependencies between events that are far apart in the sequence.- Finally, RNNs may require a lot of data to effectively train, which may not always be available.Overall, using an RNN can be advantageous for certain types of tasks, particularly those involving sequential data. However, there are also some limitations to keep in mind, and depending on the specific problem, other types of models may be more effective. . Response in Sesotho: Recurrent neural network RNN ke mofuta oa marang-rang a marang-rang a sebetsang ka ho khetheha ho sebetsana le data e latellanang hobane e na le bokhoni ba ho boloka boitsebiso bo tsoang nakong e fetileng le ho bo sebelisa ho etsa liqeto hona joale. Mona ke melemo le melemo ea ho sebelisa mohlala oa RNN: Melemo: - E ngoe ea melemo e meholo ka ho fetisisa ea ho sebelisa RNN ke bokhoni ba bona ba ho sebetsana le data e latellanang ea bolelele bo fapaneng, e leng se etsang hore e tšoanelehe hantle bakeng sa mesebetsi e kang ho lemoha puo, phetolelo ea puo. , le tlhahlobo ea maikutlo. - Li-RNN li ka boela tsa ithuta likamano tsa maemo lipakeng tsa likarolo ka tatellano, tsa li lumella ho bolela esale pele ka liketsahalo tsa nako e tlang ho latela litebello tsa nakong e fetileng. - Ka lebaka la bokhoni ba bona ba ho sebetsana le tlhahisoleseling ha nako e ntse e ea, li-RNN li khona ho bona mekhoa ea data eo ho ka bang thata ho tseba mefuta e meng ea mefuta. - Li-RNN li ka boela tsa sebelisoa ho hlahisa tatellano e mecha ea data, e ka thusang mesebetsing e kang ho hlahisa mongolo kapa ho qapa mino. Mathata: - Bothata bo bong ba ho sebelisa RNN ke bothata bo nyamelang ba gradient, bo hlahang ha tšusumetso ea liketsahalo tsa pejana ka tatellano e fokotseha haholo ha nako e ntse e feta. Sena se ka etsa hore ho be thata ho mohlala ho boloka boitsebiso bo bohlokoa mosebetsing o etsoang. - Phephetso e ngoe ea ho sebelisa li-RNN ke hore li ka bitsa chelete e ngata haholo me li nka nako ho koetlisa, haholo ha ho etsoa tatellano e telele ea data. - Li-RNN le tsona li ka na tsa thatafalloa ke ho sebetsana le litšepe lipakeng tsa liketsahalo tse arohaneng haholo ka tatellano. - Qetellong, li-RNN li ka hloka lintlha tse ngata ho koetlisa ka katleho, tse ka nang tsa se ke tsa fumaneha kamehla. Ka kakaretso, ho sebelisa RNN ho ka ba molemo bakeng sa mefuta e itseng ea mesebetsi, haholo-holo e amanang le data e latellanang. Leha ho le joalo, ho boetse ho na le mefokolo e meng eo u lokelang ho e hopola, me ho itšetlehile ka bothata bo itseng, mefuta e meng ea mehlala e ka atleha haholoanyane. 