<prompt> Hlalosa menahano ea thuto e sa tebang le thuto e tebileng.nan  <response>: Instruction in English: Explain the concepts of shallow learning and deep learning. . Response in English: Shallow learning and deep learning are two approaches to machine learning, and they differ in the depth and complexity of their architectures.Shallow learning involves training a machine learning model with a simple architecture, often with only one or two layers, using algorithms such as logistic regression, linear regression, or support vector machines. These models learn by processing the input data through the single layer of processing nodes before making a prediction. Shallow learning is well suited to problems where the data has less complex relationships or the amount of data is smaller.Deep learning, on the other hand, involves training a model with a more complex architecture, often with many layers, using algorithms such as artificial neural networks. These models learn by processing the input data through multiple layers of processing nodes, where each layer performs computations and passes on the transformed data to the next layer. Deep learning models can capture complex relationships among the data points, learn to recognize important patterns, and make more sophisticated predictions. They perform very well on high-dimensional, large scale data sets and are often used for applications like image or voice recognition.In summary, shallow learning involves training a model with a simple architecture and is well suited to problems with less complex data, while deep learning involves training a model with a deeper, more complex architecture and is well suited to problems with more complex data where it can capture more subtle and intricate relationships between data points. . Response in Sesotho: Ho ithuta ho sa tebang le ho ithuta ka botebo ke mekhoa e meli ea ho ithuta ka mochini, me e fapana ka botebo le ho rarahana ha meralo ea bona. Ho ithuta ho sa tebang ho kenyelletsa ho koetlisa moetso oa ho ithuta oa mochini o nang le boqapi bo bonolo, hangata bo nang le lera le le leng kapa tse peli feela, ho sebelisoa li-algorithms tse kang ho theola lintho, ho theoha ka mola, kapa mochini o tšehetsang vector. Mefuta ena e ithuta ka ho sebetsana le data ea ho kenya ka karolo e le ngoe ea li-node pele e bolela esale pele. Ho ithuta ho sa tebang ho loketse hantle mathata ao data e nang le likamano tse fokolang haholo kapa palo ea data e nyane. Ho ithuta ho tebileng, ka lehlakoreng le leng, ho kenyelletsa ho koetlisa mohlala o nang le meralo e rarahaneng, hangata e nang le likarolo tse ngata, ho sebelisa li-algorithms tse kang marang-rang a maiketsetso. Mehlala ena e ithuta ka ho sebetsana le data ea ho kenya ka mekhahlelo e mengata ea li-node tsa tsebetso, moo karolo e ngoe le e ngoe e etsang lipalo le ho fetisa data e fetotsoeng ho ea karolong e latelang. Mehlala ea ho ithuta e tebileng e ka hapa likamano tse rarahaneng hara lintlha tsa data, ea ithuta ho lemoha mekhoa ea bohlokoa, le ho bolela esale pele ka mokhoa o rarahaneng. Li sebetsa hantle haholo ho li-data tse phahameng, tse kholo, me hangata li sebelisoa bakeng sa litsebetso tse kang setšoantšo kapa temoho ea lentsoe. Ka kakaretso, thuto e sa tebang e kenyelletsa ho koetlisa mohlala o nang le meralo e bonolo me e loketse hantle mathata a nang le lintlha tse fokolang haholo, ha thuto e tebileng e kenyelletsa ho koetlisa mohlala o nang le mohaho o tebileng, o rarahaneng haholoanyane me o loketse hantle mathata a nang le boitsebiso bo rarahaneng moo bo leng teng. e ka hapa likamano tse poteletseng le tse rarahaneng lipakeng tsa lintlha tsa data. 