<prompt> Hlopha mefuta e fapaneng ea lintja u sebelisa marang-rang a convolutional neural.nan  <response>: Instruction in English: Classify different dog breeds using a convolutional neural network. . Response in English: To classify different dog breeds using a convolutional neural network CNN, you will need to follow these steps:1. Collect and prepare the data: Gather a dataset of labeled images of various dog breeds. The dataset should ideally have a balanced distribution of images for each breed. The images also need to be preprocessed by resizing and normalizing pixel values.2. Design the CNN architecture: Design the layers of the CNN to extract the features from the image data. The usual architecture includes multiple convolutional and pooling layers, followed by one or more fully connected layers towards the end.3. Train the model: Train the model using the prepared dataset through an optimization algorithm such as stochastic gradient descent. Define a loss function such as categorical cross-entropy to measure the error and update the weights using backpropagation.4. Evaluate the model: Evaluate the performance of the trained model on a held-out validation dataset. Use metrics such as accuracy, precision, recall, and F1-score to determine the effectiveness of the model.5. Fine-tune the model: If the performance of the model is not satisfactory, fine-tune the model by adjusting the hyperparameters, such as the number of filters, kernel size, learning rate, or changing the architecture.6. Predict on new data: Once the model is well-trained, you can use it to predict the breed of a new image of a dog. Simply pass the preprocessed image data through the trained CNN to obtain the output probabilities of each breed. Choose the breed with the highest probability as the predicted breed.This is just an outline of the process to classify different dog breeds using a convolutional neural network, there are multiple approaches, frameworks and libraries that could be used to achieve the same goal, it all depends on the specifics of the problem at hand. . Response in Sesotho: Ho arola mefuta e fapaneng ea lintja ka ho sebelisa marang-rang a convolutional neural CNN, u tla hloka ho latela mehato ena: 1. Bokella le ho lokisa lintlha: Bokella pokello ea boitsebiso bo ngotsoeng litšoantšong tsa mefuta e sa tšoaneng ea lintja. Dataset e lokela ho ba le kabo e leka-lekaneng ea litšoantšo bakeng sa mofuta o mong le o mong. Litšoantšo li boetse li hloka ho hlophisoa esale pele ka ho nchafatsa le ho etsa maemo a tloaelehileng a pixel. 2. Etsa moralo oa CNN: Rala likarolo tsa CNN ho ntša likarolo ho data ea setšoantšo. Meaho e tloaelehileng e kenyelletsa likarolo tse ngata tsa convolutional le pooling, tse lateloang ke lera le le leng kapa ho feta tse hokahaneng ka botlalo ho ea qetellong. 3. Koetlisa mohlala: Koetlisa mohlala u sebelisa pokello ea boitsebiso e lokisitsoeng ka algorithm ea ho ntlafatsa joalo ka ho theoha ha stochastic gradient. Hlalosa tsebetso ea tahlehelo joalo ka categorical cross-entropy ho lekanya phoso le ho nchafatsa litekanyo ka ho sebelisa li-backpropagation. 4. Lekola mohlala: Lekola tsebetso ea mohlala o koetlisitsoeng ho dataset ea netefatso e tšoaretsoeng. Sebelisa metrics joalo ka ho nepahala, ho nepahala, ho hopola, le lintlha tsa F1 ho fumana katleho ea mohlala. 5. Lokisa mohlala hantle: Haeba tsebetso ea mohlala e sa khotsofatse, lokisa mohlala hantle ka ho fetola li-hyperparameter, tse kang palo ea li-filters, boholo ba kernel, tekanyo ea ho ithuta, kapa ho fetola mohaho. 6. Etsa esale pele ka lintlha tse ncha: Hang ha mohlala o koetlisitsoe hantle, u ka o sebelisa ho bolela esale pele mofuta oa setšoantšo se secha sa ntja. Fetisetsa feela lintlha tsa litšoantšo tse fetisitsoeng pele ho CNN e koetlisitsoeng ho fumana menyetla ea tlhahiso ea mofuta o mong le o mong. Khetha mofuta o nang le monyetla o moholo ka ho fetisisa e le mofuta o boletsoeng esale pele. Ena e mpa e le kemiso ea tsebetso ea ho arola mefuta e fapaneng ea lintja ho sebelisa marang-rang a convolutional neural, ho na le mekhoa e mengata, mekhoa le lilaebrari tse ka sebelisoang ho finyella sepheo se le seng, ho itšetlehile ka lintlha tse tobileng tsa bothata bo teng. 