<prompt> Mpolelle hore na u ka lokisa mofuta oa AI joangnan  <response>: Instruction in English: Tell me how you would debug an AI model . Response in English: Debugging an AI model can be a complex process, but there are several steps you can take to identify and resolve issues.1. Identify the problem: Start by defining the problem with the AI model. Is the model not performing well, or is it not producing accurate results? Knowing the issue will help determine the most effective way to resolve it.2. Check the data: The quality and accuracy of the data used to train the AI model can significantly impact its performance. Make sure the data is correctly labeled, accurate, and free of bias. Also, ensure that there are no missing or incorrect values.3. Review the model architecture: Ensure that the models architecture is appropriate for the problem its trying to solve. Check the number of layers, the type of activation functions, and the loss function used.4. Analyze the models performance: Use evaluation metrics such as accuracy, precision, recall, and F1 score to measure the models performance. Plotting the models learning curve can also help you identify if the model is underfitting or overfitting.5. Fine-tune the model: If the model is underperforming, you may need to fine-tune it. You can try adjusting the models hyperparameters, such as the learning rate, batch size, or the number of hidden layers, to see if performance improves.6. Review the code: Ensure that there are no programming errors in the code for the AI model. Syntax errors or incorrect variable assignments can lead to unexpected behavior and poor performance.7. Seek help: If you are unable to resolve the problem after trying the steps above, consider seeking help from colleagues or the AI community. They might have encountered similar problems and can provide insights and potential solutions. . Response in Sesotho: Ho lokisa mokhoa oa AI e ka ba tsebetso e rarahaneng, empa ho na le mehato e mengata eo u ka e nkang ho tseba le ho rarolla mathata. 1. Hlalosa bothata: Qala ka ho hlalosa bothata ka mohlala oa AI. Na mohlala ha o sebetse hantle, kapa ha o hlahise liphetho tse nepahetseng? Ho tseba taba ho tla thusa ho fumana tsela e molemohali ea ho e rarolla. 2. Hlahloba lintlha: Boleng le ho nepahala ha lintlha tse sebelisoang ho koetlisa mohlala oa AI li ka ama tsebetso ea eona haholo. Netefatsa hore lintlha li ngotsoe ka nepo, li nepahetse, me ha li na leeme. Hape, etsa bonnete ba hore ha ho na litekanyetso tse sieo kapa tse fosahetseng. 3. Hlahloba kaho ea mohlala: Netefatsa hore moetso oa meralo o loketse bothata boo o lekang ho bo rarolla. Lekola palo ea lira, mofuta oa tsebetso ea tsebetso, le tsebetso ea tahlehelo e sebelisitsoeng. 4. Sekaseka tsebetso ea mohlala: Sebelisa metrics ea tlhahlobo e kang ho nepahala, ho nepahala, ho hopola, le lintlha tsa F1 ho lekanya tsebetso ea mohlala. Ho rala mokhoa oa ho ithuta oa mohlala ho ka u thusa ho tseba hore na mohlala ha oa lekana kapa o fetelletse. 5. Beakanya mohlala: Haeba mohlala o sa sebetse hantle hantle, ho ka na ha hlokahala hore u o lokise hantle. U ka leka ho lokisa li-hyperparameter tsa mohlala, joalo ka sekhahla sa ho ithuta, boholo ba batch, kapa palo ea likarolo tse patiloeng, ho bona hore na tsebetso ea ntlafala. 6. Hlahloba khoutu: Etsa bonnete ba hore ha ho na liphoso tsa lenaneo ka khoutu bakeng sa mohlala oa AI. Liphoso tsa syntax kapa likabelo tse sa nepahalang tse fapaneng li ka lebisa boitšoarong bo sa lebelloang le tsebetsong e mpe. 7. Batla thuso: Haeba u sa khone ho rarolla bothata ka mora ho leka mehato e ka holimo, nahana ka ho batla thuso ho basebetsi-moho kapa sechaba sa AI. E ka na eaba ba ile ba kopana le mathata a tšoanang me ba ka fana ka temohisiso le tharollo e ka bang teng. 