<prompt> Ke mehato efe ea ho kenya tsebetsong marang-rang a convolutional neural?nan  <response>: Instruction in English: What are the steps to implement a convolutional neural network? . Response in English: A convolutional neural network CNN is a type of artificial neural network commonly used in image recognition and processing. Here are the main steps to implement a CNN:1. Data acquisition and pre-processing: Gather a large dataset of labeled images, and split them into training and validation sets. Pre-process the images, for instance by normalizing their pixel values, and possibly applying data augmentation.2. Define the model architecture: Design the layers and hyperparameters of the CNN. A typical architecture includes several convolutional layers, with filters of various sizes, followed by non-linear activation functions, pooling layers, and optionally, batch normalization or dropout layers. The exact architecture depends on the problem at hand.3. Initialize weights: Initialize the weights of the CNN randomly or using some pre-trained weights.4. Forward Propagation: Pass the input image through the network, calculating the output of each layer.5. Calculate the loss: Compute the error between the predicted label and the true label of the image. This is typically done using a loss function such as cross-entropy.6. Backpropagation: Backpropagate the error through the network, computing the gradient of the loss with respect to each weight.7. Optimization: Update the weights using an optimization algorithm such as Stochastic Gradient Descent, Adam or RMSprop.8. Monitor and evaluate: Monitor the training and validation loss during the training process, and evaluate the performance of the trained model on the validation set, using relevant metrics such as accuracy, precision, and recall.9. Fine-tune: Fine-tune the model by adjusting the hyperparameters, the architecture, or the optimization algorithm, to improve its performance on the validation set.10. Test: Evaluate the final model on a separate test set. This gives an estimate of the models ability to generalize to unseen data.These are the basic steps for training and evaluating a convolutional neural network, however, there can be variations depending on the specific problem and dataset. . Response in Sesotho: Convolutional neural network CNN ke mofuta oa neural network ea maiketsetso e sebelisoang hangata ho elelloa le ho lokisa litšoantšo. Mehato ea mantlha ea ho kenya tšebetsong CNN ke ena: 1. Ho fumana lintlha le ho sebetsa esale pele: Bokella pokello ea data e kholo ea litšoantšo tse ngotsoeng, me u li arole ka lihlopha tsa koetliso le netefatso. Etsa litšoantšo esale pele, mohlala ka ho etsa hore boleng ba pixel bo tloaelehe, me mohlomong u sebelise katoloso ea data. 2. Hlalosa sebopeho sa moetso: Rala likarolo le li-hyperparameter tsa CNN. Meaho e tloaelehileng e kenyelletsa likarolo tse maloa tsa convolutional, tse nang le li-filters tsa boholo bo fapaneng, tse lateloang ke tsebetso ea tsebetso e sa lekanyetsoang, likarolo tsa ho kopanya, le ka boikhethelo, mekhahlelo e tloaelehileng ea batch kapa ea ho tlohela mosebetsi. Sebopeho se nepahetseng hantle se itšetlehile ka bothata bo teng. 3. Simolola litekanyo: Qala litekanyo tsa CNN ka mokhoa o sa reroang kapa ka ho sebelisa litekanyo tse seng li koetlisitsoe. 4. Ho Phatlalatsa Pele: Fetisa setšoantšo se kenyang marang-rang, u bala tlhahiso ea lera ka leng. 5. Bala tahlehelo: Bala phoso pakeng tsa leibole e boletsoeng esale pele le leibole ea nete ea setšoantšo. Hangata sena se etsoa ho sebelisoa tsebetso ea tahlehelo joalo ka cross-entropy. 6. Phatlalatso ea morao-rao: Fetola phoso ka marang-rang, u bapisa sekhahla sa tahlehelo mabapi le boima bo bong le bo bong. 7. Katoloso: Ntlafatsa boima ka ho sebelisa algorithm ea ho ntlafatsa joalo ka Stochastic Gradient Descent, Adam kapa RMSprop. 8. Sheba le ho hlahloba: Lekola tahlehelo ea koetliso le netefatso nakong ea thupelo, me u hlahlobe tsebetso ea mohlala o koetlisitsoeng sethaleng sa netefatso, u sebelisa metrics e amehang joalo ka ho nepahala, ho nepahala le ho hopola. 9. Tumisa hantle: Beakanya mohlala ka ho lokisa li-hyperparameter, moralo, kapa algorithm ea ho ntlafatsa, ho ntlafatsa tsebetso ea ona ho sete ea netefatso. 10. Teko: Lekola mohlala oa ho qetela ka sete e fapaneng ea teko. Sena se fana ka khakanyo ea bokhoni ba mohlala oa ho kopanya data e sa bonahaleng. Tsena ke mehato ea mantlha ea ho koetlisa le ho lekola marang-rang a convolutional neural, leha ho le joalo, ho ka ba le mefuta e fapaneng ho latela bothata bo itseng le dataset. 