<prompt> Theha algorithm bakeng sa ho lemoha linomoro tse ngotsoeng ka letsoho u sebelisa netweke ea convolutional neuralnan  <response>: Instruction in English: Create an algorithm for recognizing handwritten numbers using a convolutional neural network . Response in English: A convolutional neural network CNN is a type of deep neural network commonly used in image recognition and processing tasks. These networks have been successful in recognizing patterns in images and are often used for tasks such as recognizing handwritten numbers.Here is an algorithm for recognizing handwritten numbers using a convolutional neural network:1. Collect and preprocess data: Collect a large dataset of handwritten digits labeled with the corresponding number. Preprocess the data by resizing the images, normalizing the pixel values, and splitting the dataset into training, validation, and testing sets.2. Build the CNN architecture: Design the architecture of the CNN, typically composed of convolutional layers, pooling layers, and fully-connected layers. The exact architecture may vary depending on the problem and dataset.3. Initialize the weights: Initialize the weights of the CNN randomly or using a pre-trained model.4. Train the model: Train the CNN on the training set by iterating over the data, performing a forward pass to compute the prediction, calculating the loss, and then updating the weights using backpropagation.5. Evaluate the model: Evaluate the performance of the CNN on the validation set during training to monitor progress and avoid overfitting. Once the training is complete, evaluate the performance on the test set to obtain an unbiased estimate of the models accuracy.6. Fine-tune the model: Fine-tune the model by adjusting the hyperparameters, such as the learning rate, batch size, and number of epochs, based on the performance on the validation set.7. Make predictions: Once the model has been trained and fine-tuned, it can be used to make predictions on new images of handwritten digits by performing a forward pass and returning the predicted number. . Response in Sesotho: Convolutional neural network CNN ke mofuta oa marang-rang a tebileng a neural a atisang ho sebelisoa mesebetsing ea ho lemoha le ho lokisa litšoantšo. Marang-rang ana a atlehile ho lemoha mekhoa ea litšoantšo me hangata a sebelisetsoa mesebetsi e kang ho lemoha linomoro tse ngotsoeng ka letsoho. Mona ke algorithm bakeng sa ho lemoha linomoro tse ngotsoeng ka letsoho ho sebelisa convolutional neural network: 1. Bokella me u lokise data esale pele: Bokella dataset e kholo ea linomoro tse ngotsoeng ka letsoho tse ngotsoeng ka nomoro e tsamaisanang. Tsoela pele ho datha ka ho fetola boholo ba litšoantšo, ho etsa hore boleng ba pixel bo tloaelehe, me u arole dataset hore e be lithupelo, netefatso le lihlopha tsa liteko. 2. Haha moralo oa CNN: Rala moralo oa CNN, oo hangata o nang le likarolo tsa convolutional, pooling layers, le likarolo tse hokahaneng ka botlalo. Sebopeho se nepahetseng se ka fapana ho latela bothata le dataset. 3. Qala litekanyo: Qala litekanyo tsa CNN ka mokhoa o sa reroang kapa ka ho sebelisa mohlala o koetlisitsoeng esale pele. 4. Koetlisa mohlala: Koetlisa CNN ka koetliso e behiloeng ka ho pheta-pheta holima data, ho etsa phallo ea pele ho bala se boletsoeng esale pele, ho bala tahlehelo, le ho ntlafatsa litekanyo ho sebelisa li-backpropagation. 5. Hlahlobisisa mohlala: Hlahloba tsebetso ea CNN mabapi le tiiso e behiloeng nakong ea koetliso ho shebella tsoelo-pele le ho qoba ho feta tekano. Hang ha koetliso e phethiloe, hlahloba tsebetso ea tlhahlobo ho fumana khakanyo e sa lebelloang ea ho nepahala ha mohlala. 6. Beakanya mohlala hantle: Lokisa mohlala ka ho lokisa li-hyperparameter, joalo ka sekhahla sa ho ithuta, boholo ba batch, le palo ea linako, ho latela tsebetso ho sete ea netefatso. 7. Etsa likhakanyo: Hang ha mohlala o se o koetlisitsoe le ho lokisoa hantle, o ka sebelisoa ho bolela esale pele litšoantšong tse ncha tsa linomoro tse ngotsoeng ka letsoho ka ho etsa phallo ea pele le ho khutlisa nomoro e boletsoeng esale pele. 