<prompt> Hlahisa tafole e bontsang papiso ea Mekhoa ea ho Ithuta ka Mechini le Mekhoa e Tebileng ea ho Ithuta.nan  <response>: Instruction in English: Generate a table showing the comparison of Machine Learning and Deep Learning techniques. . Response in English: Criterion Machine Learning Deep Learning --------- Data Effective with small datasets. Effective with big datasets. Algorithms Algorithms with explicit instructions. Algorithms that use multiple processing layers with unsupervised learning. Hardware Can be performed with a regular computer. Mostly require powerful computing resources e.g. GPUs. Architecture Shallow neural networks up to 3 layers Deep neural networks more than 3 layers Feature Extraction Requires manual Feature Extraction Automatically learns the features during learning phase. Accuracy Good accuracy with simple data sets. Improves accuracy as the data size increases. Learning Type Mainly supervised learning. Works on either supervised or unsupervised learning. Applications Spam detection, Stock market analysis, Recommendation systems. Image recognition, Speech recognition, Natural language processing. . Response in Sesotho: Criterion Ho Ithuta ka Mechini Thuto e Tebileng --- ------ --- --- Lintlha E sebetsa ka li-dataset tse nyane. E sebetsa ka li-datasets tse kholo. Algorithms Algorithms e nang le litaelo tse hlakileng. Lialgorithm tse sebelisang mekhahlelo e mengata ea tsebetso ka thuto e sa laoleheng. Lisebelisoa E ka etsoa ka komporo e tloaelehileng. Hangata e hloka lisebelisoa tse matla tsa komporo mohlala, li-GPU. Architecture Marang-rang a sa tebang a methapo ho fihla ho 3 Marang-rang a tebileng a methapo mekhahlelo e fetang 3 Feature Extraction E hloka bukana Feature Extraction E ithuta likarolo ka bo eona nakong ea ho ithuta. Nepahetse Ho nepahala hantle ka lisebelisoa tse bonolo tsa data. E ntlafatsa ho nepahala ha boholo ba data bo ntse bo eketseha. Mofuta oa ho Ithuta Thuto e hlokometsoeng haholo. E sebetsa thutong e laolwang kapa e sa behwang leihlo. Lisebelisoa Ho lemoha spam, Tlhahlobo ea maraka oa maraka, Sistimi ea likhothaletso. Temogo ya seswantšho, Temogo ya Polelo, Puo ya tlhago. 