<prompt> Bapisa le ho bapisa ho ithuta ka mochini khahlano le ho ithuta ho tebileng.nan  <response>: Instruction in English: Compare and contrast machine learning vs deep learning. . Response in English: Machine learning and deep learning are both subfields of AI that aim to make machines intelligent by training models to recognize patterns or make decisions with data input. These methods have revolutionized many industries by enabling computers to perform tasks once thought only possible by humans. However, there are some notable differences between the two approaches.Machine learning is an umbrella term that includes a wide variety of techniques for building models that can learn from data. These techniques include linear regression, decision trees, support vector machines, and more. Typically, a machine learning model is trained on a dataset by adjusting its internal parameters to minimize the error of its predictions. Once trained, the model can be used to make predictions on new data. Machine learning is good for tasks where the relationship between the input and output is relatively simple and can be captured by a mathematical equation.Deep learning, on the other hand, is a specialized subset of machine learning, that focuses on the development and training of neural networks. These networks are composed of layers of interconnected nodes, with the ability to automatically extract and learn hierarchical representations of data. Deep learning requires large amounts of data and powerful computing resources to train the model. Deep learning models are especially good for image, speech, and text recognition, as it can capture highly complex and non-linear relationships between inputs and outputs.In summary, machine learning and deep learning differ in the complexity of the models they use, the type of problems they are best suited for, and the amount of data and computational resources required for training. While machine learning encompasses a wider variety of techniques, deep learning is more specialized and powerful for certain types of problems. . Response in Sesotho: Ho ithuta ka mochini le ho ithuta ka botebo ke likarolo tse nyane tsa AI tse ikemiselitseng ho etsa hore mechini e be bohlale ka mehlala ea koetliso ho lemoha lipaterone kapa ho etsa liqeto ka ho kenya data. Mekhoa ena e fetotse liindasteri tse ngata ka ho nolofalletsa likhomphieutha ho etsa mesebetsi eo pele ho neng ho nahanoa hore e ka khoneha feela ke batho. Leha ho le joalo, ho na le phapang e hlokomelehang lipakeng tsa mekhoa ena e meli. Ho ithuta ka mochini ke sekhele se kenyelletsang mekhoa e mengata e fapaneng ea ho aha mehlala e ka ithutang ho data. Mekhoa ena e kenyelletsa ho fokotseha ha mela, lifate tsa liqeto, mechini ea li-vector ea tšehetso, le tse ling. Ka tloaelo, mohlala oa ho ithuta ka mochini o koetlisetsoa pokellong ea data ka ho lokisa maemo a eona a kahare ho fokotsa phoso ea likhakanyo tsa ona. Ha e se e koetlisitsoe, mohlala o ka sebelisoa ho bolela esale pele ka data e ncha. Ho ithuta ka mochini ho molemo bakeng sa mesebetsi moo kamano lipakeng tsa sephetho le se hlahisoang e batlang e le bonolo me e ka nkuoa ke lipalo tsa lipalo. Ho ithuta ho tebileng, ka lehlakoreng le leng, ke karolo e ikhethileng ea ho ithuta ka mochini, e shebaneng le ntsetsopele le koetliso ea marang-rang a neural. Marang-rang ana a entsoe ka likarolo tsa li-node tse hokahaneng, tse nang le bokhoni ba ho ntša le ho ithuta liphatlalatso tsa maemo a phahameng a data. Ho ithuta ho tebileng ho hloka lintlha tse ngata le lisebelisoa tse matla tsa khomphieutha ho koetlisa mohlala. Mehlala ea ho ithuta e tebileng e ntle haholo bakeng sa setšoantšo, puo, le temoho ea mongolo, kaha e ka hapa likamano tse rarahaneng haholo le tse se nang moeli lipakeng tsa lintho tse kenang le tse hlahisoang. Ka kakaretso, ho ithuta ka mochini le ho ithuta ka botebo ho fapana ka ho rarahana ha mefuta eo ba e sebelisang, mofuta oa mathata ao ba a loketseng hantle, le palo ea lintlha le lisebelisoa tsa computational tse hlokahalang bakeng sa koetliso. Leha ho ithuta ka mochini ho akaretsa mekhoa e mengata e fapaneng, thuto e tebileng e ikhethile ebile e matla bakeng sa mefuta e itseng ea mathata. 