<prompt> Ngola algorithm ea lihlopha bakeng sa ho lemoha linomoro tse ngotsoeng ka letsoho.nan  <response>: Instruction in English: Write a classification algorithm for recognizing handwritten digits. . Response in English: Recognizing handwritten digits can be achieved by using various techniques in machine learning and deep learning.Heres an example of a classification algorithm for recognizing handwritten digits through the use of a convolutional neural network CNN:1. Data Preparation: The first step involves collecting and preparing the dataset of handwritten digits. This data will be split into training and test datasets, consisting of images of numbers 0-9. Each image will be preprocessed to normalize pixel values, resize and convert images to grayscale. 2. Building the Model:A CNN model will be implemented, consisting of multiple layers that include: a Convolutional layers to extract features by performing a convolution operation while preserving spatial relationships between pixels b Pooling layers to reduce the spatial size of the image and allow for assumptions to be made about features in the image c Fully connected layers to produce a predicted output3. Training the Model:The model will be trained using the training dataset. This will involve defining loss function and an optimization algorithm to minimize the loss. We will also set the number of epochs to train for and batch size for mini-batch gradient descent.4. Evaluating the Model:Using the test dataset, the accuracy of the model will be evaluated to see how well the model generalizes on unseen data.5. Predictions:With the trained model, we can now make predictions on new handwritten digits by passing images through our CNN to classify it into one of 10 classes 0-9.This is just one example of a classification algorithm for recognizing handwritten digits. There are a variety of other techniques and models that can be explored and used to achieve this task. . Response in Sesotho: Ho lemoha linomoro tse ngotsoeng ka letsoho ho ka finyelloa ka ho sebelisa mekhoa e fapaneng ea ho ithuta ka mochine le ho ithuta ka botebo. Mona ke mohlala oa algorithm ea ho hlophisa bakeng sa ho lemoha linomoro tse ngotsoeng ka letsoho ka tšebeliso ea marang-rang a convolutional neural CNN: 1. Tokisetso ea Boitsebiso: Mohato oa pele o kenyelletsa ho bokella le ho lokisa dataset ea linomoro tse ngotsoeng ka letsoho. Lintlha tsena li tla aroloa ho li-dataset tsa koetliso le tsa liteko, tse nang le litšoantšo tsa linomoro 0-9. Setšoantšo se seng le se seng se tla etsoa esale pele ho etsa hore boleng ba pixel bo tloaelehe, bo fetole boholo le ho fetolela litšoantšo ho grayscale. 2. Ho aha Mohlala: Ho tla kengoa tsebetsong mohlala oa CNN, o nang le lihlopha tse ngata tse kenyeletsang: a Convolutional layers ho ntša likarolo ka ho etsa tsebetso ea convolution ha ho ntse ho bolokoa likamano tsa sebaka pakeng tsa pixels b Ho kopanya likarolo ho fokotsa boholo ba sebaka sa setšoantšo. le ho lumella hore ho etsoe maikutlo a mabapi le likarolo tse setšoantšong c Mekhahlelo e hokahaneng ka botlalo ho hlahisa sephetho se boletsoeng esale pele 3. Koetliso ea Mohlala: Mohlala o tla koetlisoa ho sebelisoa dataset ea koetliso. Sena se tla kenyelletsa ho hlalosa tsebetso ea tahlehelo le algorithm ea optimization ho fokotsa tahlehelo. Hape re tla beha palo ea linako tsa ho ikoetlisa le boholo ba batch bakeng sa ho theoha ha mini-batch gradient. 4. Ho lekola Mohlala: Ho sebelisoa dataset ea tlhahlobo, ho nepahala ha mohlala ho tla hlahlojoa ho bona hore na mohlala o akaretsa hantle hakae ho data e sa bonahaleng. 5. Likhakanyo: Ka mohlala o koetlisitsoeng, joale re ka etsa likhakanyo tsa linomoro tse ncha tse ngotsoeng ka letsoho ka ho fetisa litšoantšo ho CNN ea rona ho e hlophisa ho e ngoe ea lihlopha tse 10 0-9. Ona ke mohlala o le mong feela oa tlhophiso ea algorithm bakeng sa ho lemoha linomoro tse ngotsoeng ka letsoho. Ho na le mefuta e fapaneng ea mekhoa le mehlala e meng e ka hlahlojoang le ho sebelisoa ho fihlela mosebetsi ona. 