<prompt> Koetlisa marang-rang a methapo ho arola mefuta e fapaneng ea liphoofolo ka nepo.nan  <response>: Instruction in English: Train a neural network to accurately classify different types of animals. . Response in English: To train a neural network to classify different types of animals, youll need to follow these steps:1. Collect data: The first step in training a neural network is collecting data. You will need many images of animals of different types. Make sure to have a diverse and large dataset with clear images and accurate labels. Each image should have an associated label indicating the type of animal in the image.2. Pre-process data: Next, youll need to pre-process your data so that it is ready for input into the neural network. This may include resizing or cropping the images, converting them to grayscale, normalizing the pixel values, and performing data augmentation.3. Split data: Divide your dataset into training, validation, and testing sets. The training set will be used to train your model, the validation set will be used to validate your model and fine-tune its hyperparameters, and the test set will be used to evaluate your model.4. Choose a model: Next, choose a model architecture that is suitable for image classification. Some popular deep learning architectures for image classification are Convolutional Neural Networks CNNs, Resnet, Inception, and EfficientNet. You can either build a model from scratch or use transfer learning and fine-tune a pre-trained model.5. Train the model: Train your model on the training dataset. Observe the accuracy and loss metrics, and adjust hyperparameters such as learning rate, batch size, and optimizer to improve the performance of the model. Use the validation set to keep track of the models performance during training.6. Evaluate the model: Once your model is trained, evaluate its performance on the test dataset. If the test accuracy is high, it indicates that the model can accurately classify different types of animals.7. Make predictions: Finally, you can use your trained model to make predictions on new images of animals by feeding the pre-processed images into the model and getting the predicted class label. . Response in Sesotho: Ho koetlisa netweke ea methapo ea kutlo ho arola mefuta e fapaneng ea liphoofolo, u tla hloka ho latela mehato ena: 1. Bokella lintlha: Mohato oa pele oa ho koetlisa netweke ea methapo ke ho bokella lintlha. U tla hloka litšoantšo tse ngata tsa liphoofolo tsa mefuta e fapaneng. Etsa bonnete ba hore u na le pokello ea lintlha tse fapaneng le tse kholo tse nang le litšoantšo tse hlakileng le lileibole tse nepahetseng. Setšoantšo se seng le se seng se lokela ho ba le label e amanang le eona e bontšang mofuta oa phoofolo e setšoantšong. 2. Boitsebiso ba pele: Ka mora moo, o tla hloka ho lokisa data ea hau esale pele hore e be malala-a-laotsoe ho kenngoa netwekeng ea methapo. Sena se ka kenyelletsa ho fetola boholo ba litšoantšo kapa ho fokotsa boholo ba litšoantšo, ho li fetolela ho grayscale, ho tloaeleha boleng ba pixel, le ho eketsa lintlha. 3. Arola lintlha: Arola datha tsa hau ka lithupelo, netefatso, le lihlopha tsa liteko. Sehlopha sa koetliso se tla sebelisoa ho koetlisa mohlala oa hau, sete sa ho netefatsa se tla sebelisoa ho netefatsa mohlala oa hau le ho lokisa li-hyperparameter tsa oona, me sete sa teko se tla sebelisoa ho hlahloba mohlala oa hau. 4. Kgetha mohlala: Ka mora moo, khetha mofuta oa moralo o loketseng ho hlophisoa ha litšoantšo. Mehaho e meng e tsebahalang ea ho ithuta e tebileng bakeng sa ho hlophisa litšoantšo ke Convolutional Neural Networks CNNs, Resnet, Inception, le EfficientNet. U ka etsa mohlala ho tloha qalong kapa ua sebelisa thuto ea phetisetso le ho hlophisa hantle mohlala o koetlisitsoeng esale pele. 5. Koetlisa mohlala: Koetlisa mohlala oa hau lethathamong la lintlha tsa koetliso. Ela hloko metrics e nepahetseng le ea tahlehelo, me u fetole li-hyperparameter tse joalo ka sekhahla sa ho ithuta, boholo ba batch, le optimizer ho ntlafatsa tsebetso ea mohlala. Sebelisa sete ea netefatso ho boloka tlaleho ea tsebetso ea mohlala nakong ea thupelo. 6. Hlahloba mohlala: Hang ha mohlala oa hau o koetlisoa, hlahloba tsebetso ea oona ho dataset ea teko. Haeba tlhahlobo e nepahetseng e phahame, e bontša hore mohlala o ka arola mefuta e fapaneng ea liphoofolo ka nepo. 7. Etsa likhakanyo: Qetellong, u ka sebelisa mohlala oa hau o koetliselitsoeng ho etsa likhakanyo ka litšoantšo tse ncha tsa liphoofolo ka ho fepa litšoantšo tse seng li entsoe mofuteng le ho fumana leibole e boletsoeng esale pele ea sehlopha. 