<prompt> Hlahisa mohlophisi ho tsebahatsa mofuta wa phoofoloSena ke setšoantšo sa tau: Sehokelo sa setšoantšo:  <response>: Instruction in English: Generate a classifier to identify the type of animal . Response in English: To generate a classifier to identify the type of animal, you will need to train a machine learning model using a dataset of animal images that are labeled with their respective animal types. This could be done by using supervised learning techniques, such as decision tree, support vector machine, or artificial neural networks.Here are the steps you could take to create an animal classifier:1. Gather a dataset: Collect or use an existing dataset of images of different animals that are labeled with their respective types. You may use datasets available online such as CIFAR-10 and ImageNet.2. Pre-process the data: Resize the images, normalize their pixel values and balance the dataset.3. Split the dataset: Divide the dataset into training, validation and test sets.4. Select a classification model: You may choose a model such as a CNN or a pre-trained model like ResNet, and fine-tune it for your specific task.5. Train the model: Train the model on the training set while monitoring its performance on the validation set.6. Test and evaluate: Test the models performance on the test set and evaluate its accuracy.7. Optimize: Experiment with different model architectures, hyperparameters or pre-processing techniques to see if you can improve the models performance.Once the classifier model has been trained and optimized, you can use it to classify new images, such as the picture of a lion provided in the input. The resulting classifier will be capable of processing the image and identifying it as a lion. . Response in Sesotho: Ho hlahisa sehlopha ho tseba mofuta oa phoofolo, o tla hloka ho koetlisa mohlala oa ho ithuta ka mochini o sebelisa pokello ea data ea litšoantšo tsa liphoofolo tse ngotsoeng ka mefuta ea tsona ea liphoofolo. Sena se ka etsoa ka ho sebelisa mekhoa ea ho ithuta e hlokometsoeng, joalo ka sefate sa liqeto, mochini o tšehetsang vector, kapa marang-rang a maiketsetso. Mehato eo u ka e nkang ke ena ho theha sehlopha sa liphoofolo: 1. Bokella pokello ea boitsebiso: Bokella kapa sebelisa dataset e teng ea litšoantšo tsa liphoofolo tse fapaneng tse ngotsoeng ka mefuta ea tsona. U ka sebelisa li-database tse fumanehang marang-rang tse kang CIFAR-10 le ImageNet. 2. Seta pele ho data: Fetola boholo ba litšoantšo, etsa hore litekanyetso tsa tsona li tloaelehe me u lekanye dataset. 3. Arola dataset: Arola dataset ka koetliso, netefatso le lihlopha tsa liteko. 4. Khetha mofuta oa lihlopha: U ka khetha mohlala o kang oa CNN kapa mohlala o koetlisitsoeng joaloka ResNet, me u o lokisetse mosebetsi oa hau o itseng. 5. Koetlisa mohlala: Koetlisa mohlala ka sete ea lithupelo ha u ntse u lekola tšebetso ea eona sete ea netefatso. 6. Leka me u hlahlobe: Lekola tsebetso ea mohlala ho sete ea liteko me u hlahlobe ho nepahala ha eona. 7. Ntlafatsa: Leka ka meralo e fapaneng ea meralo, li-hyperparameter kapa mekhoa ea pele ea ho sebetsa ho bona hore na u ka ntlafatsa tsebetso ea mohlala. Hang ha mofuta oa sehlopha o se o koetlisitsoe le ho ntlafatsoa, o ka o sebelisa ho hlophisa litšoantšo tse ncha, joalo ka setšoantšo sa tau se fanoeng ho kenyelletsong. Sephetho sa sehlopha se tla khona ho sebetsana le setšoantšo le ho se tsebahatsa e le tau. 