<prompt> Hlalosa hore na ke hobaneng ha marang-rang a tebileng a neural a le molemo haholo bakeng sa kutloisiso ea puo ea tlhaho.nan  <response>: Instruction in English: Explain why deep neural networks are particularly useful for natural language understanding. . Response in English: Deep neural networks are particularly useful for natural language understanding because they are able to learn and model complex relationships within and between input and output variables. Natural language processing NLP tasks, such as language translation, speech recognition, and sentiment analysis, require identifying patterns and relationships in data, which can be challenging due to the many nuances and intricacies of human language. Deep neural networks have multiple hidden layers, which enable them to extract and learn increasingly abstract and high-level representations of data as they process it through successive layers. This allows them to model complex structures and hierarchies within the data, as well as capture non-linear relationships between input and output variables.In NLP tasks, deep neural networks are commonly used in conjunction with other techniques, such as embedding and recurrent layers, to facilitate the processing and understanding of language. Embedding layers map words or sentences to fixed-size vectors, allowing them to be processed by the neural network in a more efficient and meaningful way. Recurrent layers, on the other hand, have memory and can maintain information across time, allowing them to process and understand sequential data such as sentences and texts. These techniques, combined with the ability of deep neural networks to learn complex relationships, make them particularly useful for natural language understanding. . Response in Sesotho: Marang-rang a tebileng a neural a bohlokoa haholo kutloisisong ea puo ea tlhaho hobane a khona ho ithuta le ho etsa mohlala oa likamano tse rarahaneng kahare le lipakeng tsa mefuta e fapaneng ea ho kenya le ho tsoa. Mesebetsi ea Tlhaho ea Puo ea Tlhaho NLP, joalo ka phetolelo ea puo, temoho ea puo, le tlhahlobo ea maikutlo, e hloka ho tsebahatsa lipaterone le likamano ho data, e ka ba phephetso ka lebaka la likarolo tse ngata tse rarahaneng le ho rarahana ha puo ea motho. Marang-rang a tebileng a neural a na le likarolo tse ngata tse patiloeng, tse li nolofalletsang ho ntša le ho ithuta liphatlalatso tse sa bonahaleng le tse phahameng tsa data ha li ntse li e sebetsa ka mekhahlelo e latellanang. Sena se ba lumella ho etsa mohlala oa libopeho tse rarahaneng le li-hierarchies ka hara data, hammoho le ho hapa likamano tse se nang moeli pakeng tsa mefuta e fapaneng ea ho kenya le ea tlhahiso. Mesebetsing ea NLP, marang-rang a tebileng a neural a atisa ho sebelisoa hammoho le mekhoa e meng, e kang ho kenya le ho pheta-pheta likarolo, ho thusa ho sebetsana le ho utloisisa puo. Ho kenya mantsoe kapa lipolelo tsa mapa ho li-vector tsa boholo bo sa fetoheng, ho li lumella hore li sebelisoe ke neural network ka tsela e sebetsang hantle le e nang le moelelo. Ka lehlakoreng le leng, likarolo tse iphetang li na le mohopolo me li khona ho boloka tlhahisoleseling ho pholletsa le nako, ho li lumella ho sebetsana le ho utloisisa lintlha tse latellanang joalo ka lipolelo le litemana. Mekhoa ena, e kopantsoeng le bokhoni ba marang-rang a tebileng a methapo ea kutlo ho ithuta likamano tse rarahaneng, li etsa hore li be molemo haholo bakeng sa kutloisiso ea puo ea tlhaho. 