<prompt> Melemo ea ho sebelisa mohlala o tebileng oa ho ithuta ho e-na le mokhoa o tloaelehileng oa ho ithuta mochine ke ofe?nan  <response>: Instruction in English: What are the benefits of using a deep learning model instead of a traditional machine learning model? . Response in English: Deep learning models offer several advantages compared to traditional machine learning models:1. Automated feature engineering: In traditional machine learning, the success of the model heavily relies on choosing the right set of features, which could take a considerable amount of time and expertise. In contrast, deep learning models can automatically learn meaningful features and complex patterns from raw data.2. Handling complex data: Deep learning can handle a wide range of data types, such as unstructured or structured data, including images, sound, and text. The hierarchical layers enable deep learning models to automatically extract high-level and abstract features from raw and complex data.3. Efficiency and scalability: Deep learning algorithms are designed to be trained on large datasets, making them more efficient at scaling to massive volumes of data. With time model becomes more accurate, reducing the need for further data preprocessing.4. Improved accuracy: Deep learning models can deliver higher accuracy compared to traditional machine learning models, particularly when working with complex data such as images, speech, or natural language text.5. Adaptability: Deep learning models can adapt to new situations and learn from them, enabling them to improve over time through continuous learning. This makes them ideal for working with dynamic environments or dealing with evolving data and emerging trends. However, it is important to note that deep learning models may require more data and computational resources to train and may not always outperform traditional machine learning models. The choice between using a traditional machine learning model or a deep learning model should be based on the complexity and nature of the data, available resources, and the desired outcome. . Response in Sesotho: Mehlala ea ho ithuta e tebileng e fana ka melemo e maloa ha e bapisoa le mefuta e tloaelehileng ea ho ithuta mochine: 1. Boenjiniere ba likarolo tse ikemetseng: Thutong ea mochine oa setso, katleho ea mohlala e itšetlehile haholo ka ho khetha likarolo tse nepahetseng, tse ka nkang nako e ngata le tsebo. Ka lehlakoreng le leng, mefuta ea ho ithuta e tebileng e ka ithuta ka bo eona likarolo tse nang le moelelo le mekhoa e rarahaneng ho tsoa ho data e tala. 2. Ho sebetsana le lintlha tse rarahaneng: Thuto e tebileng e ka sebetsana le mefuta e mengata ea boitsebiso, e kang boitsebiso bo sa hlophisoang kapa bo hlophisitsoeng, ho akarelletsa le litšoantšo, molumo le mongolo. Likarolo tsa maemo a holimo li thusa mehlala ea ho ithuta e tebileng hore e iketsetse likarolo tsa boemo bo holimo le tse sa bonahaleng ho tsoa ho data e tala le e rarahaneng. 3. Ho sebetsa hantle le ho pharalla: Litaolo tse tebileng tsa ho ithuta li etselitsoe ho koetlisoa ho li-datasets tse kholo, ho li etsa hore li sebetse hantle ha li fihla ho bongata bo boholo ba data. Ka nako ea mohlala e ba e nepahetseng haholoanyane, e fokotsang tlhokahalo ea ho tsoela pele ha data pele. 4. Ho nepahala ho ntlafetseng: Mehlala ea ho ithuta e tebileng e ka fana ka ho nepahala ha holimo ha ho bapisoa le mefuta e tloaelehileng ea ho ithuta mochine, haholo-holo ha e sebetsa ka boitsebiso bo rarahaneng bo kang litšoantšo, puo, kapa mongolo oa puo ea tlhaho. 5. Ho ikamahanya le maemo: Mehlala ea ho ithuta e tebileng e ka ikamahanya le maemo a macha le ho ithuta ho eona, ea ba nolofalletsa ho ntlafatsa ha nako e ntse e ea ka ho tsoela pele ho ithuta. Sena se etsa hore e be tse loketseng bakeng sa ho sebetsa le tikoloho e matla kapa ho sebetsana le data e tsoelang pele le mekhoa e hlahang. Leha ho le joalo, ke habohlokoa ho hlokomela hore mekhoa e tebileng ea ho ithuta e ka na ea hloka boitsebiso bo bongata le lisebelisoa tsa ho sebetsa ho koetlisa me e ka na ea se ke ea hlola e feta mekhoa e tloaelehileng ea ho ithuta mochine. Khetho pakeng tsa ho sebelisa mokhoa o tloaelehileng oa ho ithuta mochine kapa mohlala o tebileng oa ho ithuta o lokela ho ipapisa le ho rarahana le mofuta oa data, lisebelisoa tse teng, le sephetho se lakatsehang. 