<prompt> Ke liphapang life tse ka sehloohong lipakeng tsa ho ithuta ka botebo le mefuta e tloaelehileng ea ho ithuta ka mochini?nan  <response>: Instruction in English: What are the main differences between deep learning and traditional machine learning models? . Response in English: Both deep learning and traditional machine learning are methods of teaching computers to learn from data, recognizing patterns in order to make decisions and predictions. The key difference between the two lies in the model architecture and the underlying approach for extracting insights from data.Traditional machine learning is based on algorithms such as linear regression, decision trees, k-Nearest Neighbors, and support vector machines, among others. These methods typically require the training dataset to be well-curated and structured, as well as explicitly given features hand-crafted representing the raw data, so the algorithm can learn to recognize the patterns in the data while being computationally efficient.In contrast, deep learning is a subset of machine learning that involves training multi-layered neural networks. These networks can automatically learn features and representations from the raw data, often making the need of feature engineering less important. Deep learning models typically have higher computational complexity due to their depth and are able to work with large datasets and complex unstructured data such as natural language, speech, and image data. The success of deep learning has been mainly driven by the availability of powerful GPUs allowing for parallel processing, big data, and development of advanced algorithms, all working together to allow these models to achieve state-of-the-art results on many challenging machine learning problems. Nevertheless, for many simple problems, with a small dataset and clear feature space, shallow traditional models can still perform much faster, and can be easier to interpret, than deep learning models. . Response in Sesotho: Ho ithuta ka botebo le ho ithuta ka mochini oa setso ke mekhoa ea ho ruta likhomphutha ho ithuta ho tsoa ho data, ho lemoha mekhoa e le ho etsa liqeto le likhakanyo. Phapang e ka sehloohong lipakeng tsa tse peli e mabapi le meralo ea mohlala le mokhoa oa motheo oa ho fumana lintlha ho tsoa ho data. Ho ithuta ka mochini o tloaelehileng ho ipapisitse le li-algorithms tse kang ho fokotseha ha mela, lifate tsa liqeto, k-Nearest Neighbors, le mechini e tšehetsang vector, hara tse ling. Mekhoa ena hangata e hloka hore dataset ea koetliso e hlophisitsoe hantle le ho hlophisoa hantle, hammoho le likarolo tse fanoeng ka ho hlaka tse entsoeng ka letsoho tse emelang lintlha tse tala, kahoo algorithm e ka ithuta ho lemoha mekhoa ea boitsebiso ha e ntse e sebetsa ka mokhoa o sebetsang hantle. Ka lehlakoreng le leng, thuto e tebileng ke karoloana ea ho ithuta ka mochini e kenyelletsang ho koetlisa marang-rang a marang-rang a mangata. Marang-rang ana a ka ithuta likarolo le litlatsetso ho tsoa ho data e tala, hangata a etsa hore tlhoko ea boenjineri e se be bohlokoa. Hangata mefuta ea ho ithuta e tebileng e na le ho rarahana ho holimo ka lebaka la botebo ba eona me e khona ho sebetsa ka li-datasets tse kholo le lintlha tse sa hlophisoang tse kang puo ea tlhaho, puo le boitsebiso ba litšoantšo. Katleho ea thuto e tebileng e susumetsoa haholo ke ho fumaneha ha li-GPU tse matla tse lumellang tsebetso e tsoanang, data e kholo, le ntsetsopele ea li-algorithms tse tsoetseng pele, kaofela li sebetsa moho ho lumella mehlala ena ho fihlela sephetho sa boemo bo holimo ho ba bangata. mathata a ho ithuta ka mochini. Leha ho le joalo, bakeng sa mathata a mangata a bonolo, ka dataset e nyenyane le sebaka se hlakileng sa likarolo, mehlala e sa tebang ea setso e ntse e ka sebetsa ka potlako haholo, me e ka ba bonolo ho e hlalosa, ho feta mehlala e tebileng ea ho ithuta. 