<prompt> Hlalosa mokhoa oa ho theha marang-rang a maiketsetsonan  <response>: Instruction in English: Describe the process for creating an artificial neural network . Response in English: An artificial neural network is a computational model that attempts to replicate the functions of the human brain, allowing the computer to learn from and make decisions based on data. The process of creating an artificial neural network involves several steps:1. Define the problem and select network architecture: The first step in the process is to define the problem that needs to be solved and determine the type of network architecture that will be used. This could be a feed-forward network, recurrent network, or convolutional neural network, among others.2. Collect and preprocess data: Next, the data that will be used to train the neural network must be collected and preprocessed. This may involve cleaning and normalizing the data, as well as splitting it into training and validation sets.3. Initialize the network: Once the data is prepared, the neural network must be initialized. This involves setting the initial values for the networks weights and biases which can be set to small random values.4. Train the network: Training the neural network involves feeding it the input data and adjusting the weights and biases to minimize the difference between the predicted output and the actual output. This process, known as backpropagation, is done using an optimization algorithm, such as gradient descent or stochastic gradient descent. The training process continues until the network has reached an acceptable level of accuracy or until a maximum number of iterations have been completed.5. Validate the network: After training, the network is validated using the validation dataset to assess its performance. If the performance is not satisfactory, the network may be retrained or its architecture may be adjusted.6. Use the network: Once the network has been trained and validated, it can be used to make predictions on new data. The weights and biases are fixed at this point, and the neural network can be deployed to perform its intended task.The process of creating an artificial neural network is iterative and requires a lot of experimentation to find the best network architecture and hyperparameters. However, once trained, a neural network can be a powerful tool for making predictions and discovering hidden patterns in data. . Response in Sesotho: Neural network ea maiketsetso ke mohlala oa computational o lekang ho pheta-pheta mesebetsi ea boko ba motho, e lumellang komporo ho ithuta le ho etsa liqeto ho latela data. Mokhoa oa ho theha marang-rang a maiketsetso a marang-rang o kenyelletsa mehato e maloa: 1. Hlalosa bothata ebe u khetha meralo ea marang-rang: Mohato oa pele oa tsebetso ke ho hlalosa bothata bo lokelang ho rarolloa le ho fumana hore na ke mofuta ofe oa meralo ea marang-rang e tla sebelisoa. Ena e ka ba marang-rang a phepelo, marang-rang a iphetang, kapa marang-rang a convolutional neural, hara tse ling. 2. Bokella le ho lokisa data esale pele: Ka mora moo, lintlha tse tla sebelisoa ho koetlisa marang-rang a neural li tlameha ho bokelloa le ho sebetsoa esale pele. Sena se ka kenyelletsa ho hloekisa le ho etsa hore data e tloaelehe, hammoho le ho e arola ka lihlopha tsa koetliso le ho netefatsa. 3. Qala marang-rang: Hang ha boitsebiso bo lokisitsoe, marang-rang a neural a tlameha ho qalisoa. Sena se kenyelletsa ho beha litekanyetso tsa pele bakeng sa boima ba marang-rang le litšekamelo tse ka behoang ho litekanyetso tse nyenyane tse sa reroang. 4. Koetlisa marang-rang: Ho koetlisa marang-rang a marang-rang ho akarelletsa ho e fepa lintlha tsa ho kenya letsoho le ho lokisa litekanyo le litšekamelo ho fokotsa phapang pakeng tsa tlhahiso e boletsoeng esale pele le tlhahiso ea sebele. Tsebetso ena, e tsejoang ka hore ke backpropagation, e etsoa ho sebelisoa algorithm ea optimization, joalo ka ho theoha ha sekhahla kapa ho theoha ha stochastic gradient. Tsebetso ea koetliso e tsoela pele ho fihlela marang-rang a fihlile boemong bo amohelehang ba ho nepahala kapa ho fihlela palo e kholo ea ho pheta-pheta e phethiloe. 5. Netefatsa marang-rang: Ka mora koetliso, marang-rang a tiisetsoa ho sebelisa dataset ea ho netefatsa ho hlahloba tsebetso ea eona. Haeba tsebetso e sa khotsofatse, marang-rang a ka na a koetlisoa hape kapa mohaho oa oona o ka fetoloa. 6. Sebelisa marang-rang: Hang ha marang-rang a se a koetlisitsoe me a netefalitsoe, a ka sebelisoa ho etsa likhakanyo ka lintlha tse ncha. Boima le leeme li tsitsitse ntlheng ena, me marang-rang a neural a ka sebelisoa ho etsa mosebetsi o reriloeng. Mokhoa oa ho theha marang-rang a maiketsetso a neural o pheta-pheta me o hloka liteko tse ngata ho fumana mohaho o motle ka ho fetisisa oa marang-rang le li-hyperparameters. Leha ho le joalo, hang ha u koetlisitsoe, marang-rang a neural e ka ba sesebelisoa se matla sa ho bolela esale pele le ho fumana mekhoa e patiloeng ho data. 