<prompt> Ka kopo, fana ka tlhaloso e khutsoane ea phapang lipakeng tsa algorithm ea ho ithuta e tebileng le algorithm ea khale ea ho ithuta mochini.nan  <response>: Instruction in English: Please provide a brief explanation of the differences between a deep learning algorithm and a traditional machine learning algorithm. . Response in English: At a high level, the main difference between deep learning and traditional machine learning algorithms lies in their architecture or how they solve a problem.The traditional machine learning algorithms have a simpler and more rigid data processing pipeline. These algorithms, such as decision trees, logistic regression, and support vector machines, use hand-crafted rules or pre-defined mathematical formulas to model a problem, and typically have only one or two data processing layers. Thus, the traditional machine learning algorithms require feature engineering, which is the process of selecting or engineering the best input features that will improve the performance of the models.On the other hand, deep learning algorithms have a more complex architecture, comprised of many layers of interconnected nodes, commonly referred to as artificial neural networks. Each of these layers performs a simple computation and transforms the raw input data into higher level abstractions, allowing deep learning algorithms to automatically learn and extract relevant features from raw data. This means that deep learning algorithms can handle unstructured and complex data, such as images or voice recordings.In summary, deep learning algorithms have a more complex architecture and can learn features automatically from raw data, while traditional machine learning algorithms are simpler, have fewer layers of processing, and may require feature engineering to perform well. . Response in Sesotho: Boemong bo phahameng, phapang e kholo lipakeng tsa ho ithuta ho tebileng le li-algorithms tsa ho ithuta ka mochini li ka hara meaho ea bona kapa hore na ba rarolla bothata joang. Li-algorithms tsa khale tsa ho ithuta mochini li na le lipeipi tse bonolo le tse thata ho feta tsa tsebetso ea data. Litaolo tsena, joalo ka lifate tsa liqeto, mokhoa oa ho theola lintho, le mechini ea li-vector tse tšehetsang, li sebelisa melao e entsoeng ka letsoho kapa mekhoa ea lipalo e boletsoeng esale pele ho etsa mohlala oa bothata, me hangata ho na le karolo e le ngoe kapa tse peli feela tsa tsebetso ea data. Ka hona, li-algorithms tsa tloaelo tsa ho ithuta mochini li hloka boenjiniere ba likarolo, e leng mokhoa oa ho khetha kapa boenjiniere likarolo tse ntle ka ho fetisisa tsa ho kenya tse tla ntlafatsa tsebetso ea mehlala. Ka lehlakoreng le leng, li-algorithms tsa ho ithuta tse tebileng li na le meralo e rarahaneng haholoanyane, e nang le likarolo tse ngata tsa li-node tse hokahaneng, tseo hangata li bitsoang marang-rang a maiketsetso a methapo ea kutlo. E ngoe le e ngoe ea likarolo tsena e etsa khomphutha e bonolo me e fetola lintlha tse sa hlahisoang hore e be likhakanyo tsa boemo bo holimo, e lumellang li-algorithms tse tebileng tsa ho ithuta ho ithuta le ho ntša likarolo tse amehang ho data e tala. Sena se bolela hore li-algorithms tse tebileng tsa ho ithuta li ka sebetsana le data e sa hlophisoang le e rarahaneng, joalo ka litšoantšo kapa lirekoto tsa lentsoe. Ka bokhutšoanyane, li-algorithms tsa ho ithuta ka botebo li na le meralo e rarahaneng haholoanyane me li ka ithuta likarolo ka bo eona ho tsoa ho data e tala, athe mekhoa ea khale ea ho ithuta ka mochini e le bonolo, e na le likarolo tse fokolang tsa tsebetso, me e ka hloka hore boenjiniere ba likarolo bo sebetse hantle. 