<prompt> Etsa marang-rang a neural ho lemoha linomoro tse ngotsoeng ka letsoho.nan  <response>: Instruction in English: Design a neural network to recognize handwritten digits. . Response in English: To design a neural network that can recognize handwritten digits, we will need to consider the following steps:1. Preparing the data: We will use the MNIST dataset which is a popular dataset for handwritten digit recognition. It contains 60,000 training images and 10,000 test images. Each image is 28x28 pixels and represents a single digit 0-9. Before using this data, it is important to normalize the pixel values, usually by dividing by 255. The pixel values will then range between 0 and 1.2. Defining the architecture: We will use a convolutional neural network CNN for this task. CNN is a popular type of neural network for image classification tasks. The architecture of the neural network will be as follows: - Input layer: The input for the neural network will be an image of size 28x28 pixels.- Convolutional layer: The first layer will be a convolutional layer with filters to extract features from the image.- MaxPool layer: The next layer will be a MaxPool layer to reduce the dimensionality of the data, thus increasing the computational efficiency.- Dropout layer: This layer will prevent overfitting by randomly dropping out some neurons during training. - Flatten layer: This layer will flatten the previous layer into a one-dimensional array.- Dense layer: The next layer will be a fully connected layer with a specified number of neurons.- Output layer: The final layer will be an output layer with 10 neurons, each representing one of the 10 possible digits 0-9. This layer will use the softmax activation function to produce a probability distribution over the 10 classes.3. Training the model: Once the architecture is defined, we will use backpropagation to train the model using the training data. We will use cross-entropy loss function and an optimizer such as Adam to update the weights.4. Testing and Evaluation: Once training is completed, we will use the test data to evaluate the performance of the model. We can measure the accuracy of the model and tweak the architecture or training parameters to improve the performance.This is a basic overview of how to design a neural network for recognizing handwritten digits. We can further improve the performance by implementing techniques such as data augmentation, transfer learning, or hyperparameter tuning. . Response in Sesotho: Ho rala marang-rang a neural a khonang ho lemoha linomoro tse ngotsoeng ka letsoho, re tla hloka ho nahana ka mehato e latelang: 1. Ho lokisa lintlha: Re tla sebelisa MNIST dataset e leng dataset e tsebahalang bakeng sa ho lemoha linomoro tse ngotsoeng ka letsoho. E na le litšoantšo tse 60,000 tsa koetliso le litšoantšo tse 10,000 tsa liteko. Setšoantšo ka seng ke lipikselse tse 28x28 me se emela palo e le ngoe 0-9. Pele o sebelisa data ena, ho bohlokoa ho etsa hore lipalo tsa pixel li tloaelehe, hangata ka ho arola ka 255. Joale, boleng ba pixel bo tla fapana pakeng tsa 0 le 1. 2. Ho hlalosa moralo: Re tla sebelisa convolutional neural network CNN bakeng sa mosebetsi ona. . CNN ke mofuta o tsebahalang oa marang-rang a neural bakeng sa mesebetsi ea ho hlophisa litšoantšo. Sebopeho sa marang-rang sa methapo e tla ba ka tsela e latelang: - Lera la ho kenya: Kenyelletso bakeng sa marang-rang a methapo e tla ba setšoantšo sa boholo ba 28x28 pixels. - Convolutional layer: Lera la pele e tla ba lesela la convolutional le li-filters ho ntša likarolo setšoantšong. - Lera la MaxPool: Lera le latelang e tla ba lera la MaxPool ho fokotsa boholo ba data, kahoo ho eketsa katleho ea computational. - Dropout layer: Lera lena le tla thibela ho tlala ho feta tekano ka ho lahla li-neuron tse ling nakong ea boikoetliso. - Lera le bataletseng: Lera lena le tla batalatsa karolo e fetileng hore e be karolo e lekanang le ngoe. - Lera le teteaneng: Lera le latelang e tla ba lera le hokahaneng ka botlalo le palo e boletsoeng ea li-neurone. - Lera la sephetho: Lera la ho qetela e tla ba lesela la tlhahiso le nang le li-neurone tse 10, e ngoe le e ngoe e emela e ngoe ea linomoro tse 10 tse ka khonehang 0-9. Lera lena le tla sebelisa tsebetso ea tsebetso ea softmax ho hlahisa phepelo ea monyetla ho feta lihlopha tse 10. 3. Koetlisa mohlala: Hang ha mohaho o hlalosoa, re tla sebelisa mokhoa oa ho khutlisetsa morao ho koetlisa mohlala ho sebelisa lintlha tsa koetliso. Re tla sebelisa tsebetso ea tahlehelo ea cross-entropy le optimizer joalo ka Adama ho ntlafatsa litekanyo. 4. Teko le Tlhahlobo: Hang ha koetliso e phethiloe, re tla sebelisa lintlha tsa tlhahlobo ho hlahloba tsebetso ea mohlala. Re ka lekanya ho nepahala ha mohlala le ho fetola meaho kapa mekhahlelo ea koetliso ho ntlafatsa tsebetso. Ena ke kakaretso ea mokhoa oa ho rala marang-rang a neural bakeng sa ho lemoha linomoro tse ngotsoeng ka letsoho. Re ka tsoela pele ho ntlafatsa tsebetso ka ho kenya tsebetsong mekhoa e kang ho eketsa lintlha, ho ithuta ho fetisa, kapa ho fetola hyperparameter. 