<prompt> Koetlisa mohlala ho lemoha linomoro tse ngotsoeng ka letsoho.nan  <response>: Instruction in English: Train a model to recognize handwritten digits. . Response in English: To recognize handwritten digits, we can use a Convolutional Neural Network CNN - a type of deep learning algorithm commonly used for image recognition tasks. Here is an overview of the steps we can follow to train the model:1. Collect and Pre-process Data: The first step is to gather a large dataset of labeled handwritten digits. One widely available dataset is the MNIST dataset, which consists of 60,000 training images and 10,000 testing images of handwritten digits from 0-9. Next, we must pre-process the data by scaling the pixel values to a range of 0 to 1, and reshaping the images into the desired input shape of the CNN.2. Create the Model: The next step is to construct the CNN model. The model will consist of multiple layers, including convolutional layers, max-pooling layers, and fully connected layers. The convolutional layers will extract features from the images, and the fully connected layers will make the final prediction of the digit.3. Train the Model: Once the model has been created, we must train it on the training data by adjusting the models parameters to minimize the prediction error. This is typically done using an optimization algorithm such as stochastic gradient descent. During training, the model will learn to recognize patterns in the images that correspond to the digits.4. Evaluate the Model: After the model has been trained, we must evaluate its performance on the test dataset to measure how well it generalizes to new data. This is typically done by calculating the classification accuracy of the model.5. Fine-tune the Model: If the models performance is not satisfactory, we can fine-tune it by adjusting the models parameters or architecture and re-training it on the data.Once the model has been trained and fine-tuned to achieve a high level of accuracy, it can be used to recognize handwritten digits from new images. . Response in Sesotho: Ho lemoha linomoro tse ngotsoeng ka letsoho, re ka sebelisa Convolutional Neural Network CNN - mofuta oa algorithm ea ho ithuta e tebileng eo hangata e sebelisoang bakeng sa mesebetsi ea ho lemoha litšoantšo. Mona ke kakaretso ea mehato eo re ka e latelang ho koetlisa mohlala: 1. Bokella me U Sebetse Pele Lintlha: Mohato oa pele ke ho bokella dataset e kholo ea linomoro tse ngotsoeng ka letsoho. Phatlalatso e ngoe e fumanehang hohle ke MNIST dataset, e nang le litšoantšo tse 60,000 tsa koetliso le litšoantšo tse 10,000 tsa tlhahlobo ea linomoro tse ngotsoeng ka letsoho ho tloha ho 0-9. Ka mora moo, re tlameha ho sebetsana le data esale pele ka ho lekanya boleng ba pixel ho tloha ho 0 ho isa ho 1, le ho fetola litšoantšo ka mokhoa o lakatsehang oa ho kenya CNN. 2. Etsa Mohlala: Mohato o latelang ke ho aha mohlala oa CNN. Moetso ona o tla ba le mekhahlelo e mengata, ho kenyeletsoa likarolo tsa convolutional, max-pooling layering, le likarolo tse hokahaneng ka botlalo. Likarolo tsa convolutional li tla ntša likarolo litšoantšong, me lihlopha tse hokahaneng ka botlalo li tla etsa polelo ea ho qetela ea linomoro. 3. Koetlisa Mohlala: Ha mohlala o se o entsoe, re tlameha ho o koetlisa ho data ea koetliso ka ho lokisa liparamente tsa mohlala ho fokotsa phoso ea ho bolela esale pele. Hangata sena se etsoa ho sebelisoa algorithm ea optimization joalo ka stochastic gradient descent. Nakong ea koetliso, mohlala o tla ithuta ho lemoha mekhoa ea litšoantšo tse lumellanang le linomoro. 4. Hlahloba Mohlala: Ka mora hore mohlala o koetlisoe, re tlameha ho hlahloba tsebetso ea oona ho dataset ea teko ho lekanya hore na e akaretsa hantle hakae ho data e ncha. Hangata sena se etsoa ka ho bala ho nepahala ha lihlopha tsa mohlala. 5. Hlophisa Mohlala: Haeba tsebetso ea mohlala e sa khotsofatse, re ka e lokisa hantle ka ho fetola liparamente tsa mohlala kapa meralo le ho e koetlisa hape ho data. Hang ha mohlala o se o koetlisitsoe le ho lokisoa hantle ho finyella boemo bo phahameng ba ho nepahala, o ka sebelisoa ho lemoha linomoro tse ngotsoeng ka letsoho ho tsoa litšoantšong tse ncha. 