<prompt> Hlalosa litšebeliso tse 5 tse tloaelehileng tsa Thuto e Tebileng.nan  <response>: Instruction in English: Explain 5 common applications of Deep Learning. . Response in English: Deep Learning is a subset of Artificial Intelligence AI and Machine Learning ML that uses multi-layered neural networks to analyze and make sense of data. Here are five common applications of Deep Learning:1 Speech Recognition: Deep learning has played a pivotal role in increasing the accuracy of the speech recognition algorithms. Deep learning-powered systems analyze audio inputs to identify and transcript spoken words for natural language processing in speech-to-text tools, voice search, etc.2 Image Recognition: One of the most common applications of deep learning is image recognition or computer vision. Deep learning models can identify and classify various objects in pictures, enabling features like facial recognition, medical image analysis, and more.3 Natural Language Processing: Deep learning has revolutionized natural language processing which entails the human language understanding by computers. Applications include sentiment analysis, machine translation, chatbots, virtual assistants such as Siri, Cortana Google Assistant etc.4 Recommendation Systems: Deep Learning models are used by e-commerce and media streaming companies like Netflix, Amazon to dramatically improve their recommendation systems to personalize the users experience based on their reading history, searches, purchases and more.5 Predictive Analytics: Deep learning models are used to automatically uncover patterns and correlations in large datasets to generate forecasts, detect fraud, improve operations, and manage risks in various domains, for instance, stock price prediction in stock markets, patients health forecasts in the health sector, etc. . Response in Sesotho: Ho Ithuta ho Tebileng ke karoloana ea Artificial Intelligence AI le Machine Learning ML e sebelisang marang-rang a marang-rang a mangata ho sekaseka le ho etsa moelelo oa data. Mona ke mekhoa e mehlano e tloaelehileng ea ho Ithuta ho Tebileng: 1 Temoho ea Puo: Thuto e tebileng e phethile karolo ea bohlokoa ho eketseng ho nepahala ha tharabololo ea temoho ea puo. Sistimi e matla ea ho ithuta e tebileng e sekaseka litaba tsa molumo ho tseba le ho ngola mantsoe a buuoang bakeng sa ho sebetsa puo ea tlhaho ho lithulusi tsa puo ho ea ho mongolo, ho batla lentsoe, joalo-joalo. 2 Tlhokomeliso ea Litšoantšo: E ngoe ea lisebelisoa tse atileng haholo tsa ho ithuta ka botebo ke temoho ea litšoantšo kapa komporo. pono. Mehlala ea ho ithuta e tebileng e ka khetholla le ho hlophisa lintho tse fapaneng litšoantšong, e nolofalletsa likarolo tse kang ho lemoha sefahleho, tlhahlobo ea litšoantšo tsa bongaka, le tse ling. 3 Tsebetso ea Puo ea Tlhaho: Ho ithuta ka botebo ho fetotse tsebetso ea puo ea tlhaho e kenyelletsang kutloisiso ea puo ea batho ke likhomphutha. Likopo li kenyeletsa tlhahlobo ea maikutlo, phetolelo ea mochine, li-chatbots, bathusi ba sebele ba kang Siri, Cortana Google Assistant joalo-joalo 4 Mekhoa ea Litlhahiso: Mekhoa e tebileng ea ho Ithuta e sebelisoa ke likhamphani tsa e-commerce le mecha ea phatlalatso e kang Netflix, Amazon ho ntlafatsa haholo mekhoa ea bona ea likhothaletso. ho etsa boiphihlelo ba mosebelisi ho latela nalane ea bona ea ho bala, seo ba se batlileng, seo ba se rekileng le tse ling. 5 Litlhahlobo tsa Predictive: Mehlala ea ho ithuta e tebileng e sebelisoa ho utulla ka bo eona lipaterone le likhokahano ho li-dataset tse kholo ho hlahisa ponelopele, ho bona bosholu, ho ntlafatsa tsebetso, le ho laola likotsi libakeng tse fapaneng, mohlala, ho bolela esale pele ka theko ea setoko limmarakeng tsa setoko, likhakanyo tsa bophelo bo botle ba mokuli. lefapha la bophelo bo botle, joalo-joalo. 