<prompt> Thathamisa mathata a ho ithuta ka botebo.nan  <response>: Instruction in English: List the challenges of deep learning. . Response in English: 1. Large dataset requirement: Deep learning requires massive amounts of data to train effectively. The availability of high-quality labeled data is a significant challenge when it comes to building powerful deep learning models.2. Computational power: Deep learning models can be very complex with millions of parameters, resulting in a need for significant computational resources. Training deep learning models can be time-consuming and computationally intensive.3. The black box nature: deep learning algorithms are often referred to as black boxes since their internal workings are quite opaque to developers, which makes it challenging to understand precisely how such algorithms make their decisions.4. Overfitting: Deep learning models, especially those with many parameters, are prone to overfitting, which means they may perform well on the training data but poorly on new data.5. Difficulty in optimizing: Deep learning models are difficult to optimize because of the many hyperparameters that must be set correctly to achieve high performance.6. Lack of transferability: Deep learning models trained on one task are not always easily transferable to a different task, making it challenging to apply pre-trained models to new problems.7. Adversarial attacks: Deep learning models are susceptible to adversarial attacks, where small perturbations to the input data can cause the model to make incorrect predictions. . Response in Sesotho: 1. Tlhokahalo e kholo ea pokello ea data: Ho ithuta ka botebo ho hloka lintlha tse ngata ho ikwetlisa ka katleho. Ho fumaneha ha data ea boleng bo holimo e ngotsoeng ke phephetso e kholo ha ho tluoa tabeng ea ho aha mefuta e matla ea ho ithuta e tebileng. 2. Matla a khomphutha: Mehlala ea ho ithuta e tebileng e ka ba thata haholo ka li-parameter tse limilione, e leng se etsang hore ho be le tlhokahalo ea lisebelisoa tsa bohlokoa tsa computational. Ho koetlisa mekhoa e tebileng ea ho ithuta ho ka nka nako le ho ba matla haholo. 3. Sebopeho sa lebokose le letšo: li-algorithms tse tebileng tsa ho ithuta hangata li bitsoa mabokose a matšo kaha tsebetso ea bona ea ka hare ha e na leseli ho bahlahisi, e leng se etsang hore ho be thata ho utloisisa hantle hore na li-algorithms tse joalo li etsa liqeto tsa bona joang. 4. Overfitting: Mehlala ea ho ithuta e tebileng, haholo-holo e nang le li-parameter tse ngata, e na le tšekamelo ea ho feta, ho bolelang hore e ka sebetsa hantle ho data ea koetliso empa e sa sebetse hantle ho data e ncha. 5. Bothata ba ho ntlafatsa: Mehlala ea ho ithuta e tebileng e thata ho ntlafatsa ka lebaka la li-hyperparameter tse ngata tse lokelang ho behoa ka nepo ho finyella tsebetso e phahameng. 6. Ho hloka phetiso: Mehlala ea ho ithuta e tebileng e koetlisitsoeng mosebetsing o le mong ha se kamehla e fetisetsoang habonolo mosebetsing o fapaneng, e leng ho etsang hore ho be thata ho sebelisa mehlala e koetlisitsoeng pele ho mathata a macha. 7. Litlhaselo tsa Bahanyetsi: Mehlala ea ho ithuta e tebileng e ka hlaseloa ke litlhaselo tsa bahanyetsi, moo ho ferekanya ho fokolang ho data ea ho kenya ho ka etsang hore mohlala o etse likhakanyo tse fosahetseng. 