<prompt> Hlalosa mehato e amehang ho theheng mohlala oa neural network.nan  <response>: Instruction in English: Describe the steps involved in creating a neural network model. . Response in English: Creating a neural network model involves several steps, including:1. Defining the problem and preparing the data: The first step in creating a neural network model is to define the problem you want the model to solve, such as a classification or regression task. Once you have defined the problem, you should prepare the data by collecting, cleaning and pre-processing it for use in training the model.2. Determining the model architecture: Next, you should determine the architecture of your neural network. This involves deciding on the number of layers in the network, the number of neurons in each layer, and the type of activation function to use at each layer.3. Initializing the weights: The next step is to initialize the weights for the neurons in the network. This is typically done randomly at the beginning of the training process, with the aim of finding the optimal weights through an iterative learning process.4. Training the model: Once the weights have been initialized, the model can be trained on the data using an algorithm like backpropagation. During training, the weights of the neurons are adjusted as the model learns from the data. This generally involves iterating over the data multiple times and updating the weights as the model makes predictions and receives feedback on its performance.5. Evaluating the model: After the model has been trained, you should evaluate its performance on a validation dataset. This will help you to assess how well the model is likely to perform on new, unseen data.6. Fine-tuning the model: Based on the evaluation results, you may decide to fine-tune the model by adjusting the architecture, the learning rate or other hyperparameters. This can help to improve the models performance on the validation dataset.7. Making predictions: After the model has been trained and fine-tuned, it can be used to make predictions on new, unseen data. You can use the trained model to make predictions for individual examples or to make predictions on a batch of data.In summary, creating a neural network model involves defining the problem, preparing the data, determining the architecture, initializing the weights, training the model, evaluating its performance, fine-tuning the model, and finally making predictions with the trained model. . Response in Sesotho: Ho theha mohlala oa neural network ho kenyelletsa mehato e mengata, ho kenyelletsa: 1. Ho hlalosa bothata le ho lokisa data : Mohato oa pele oa ho theha mokhoa oa neural network ke ho hlalosa bothata boo u batlang hore mohlala o bo rarolle, joalo ka ho arola. kapa mosebetsi wa ho kgutlela morao. Ha u se u hlalositse bothata, u lokela ho lokisa lintlha ka ho li bokella, ho li hloekisa le ho li lokisa esale pele hore li sebelisoe ho koetlisa mohlala. 2. Ho etsa qeto ea sebopeho sa mohlala : Ka mora moo, u lokela ho tseba sebopeho sa marang-rang a methapo ea hau. Sena se kenyelletsa ho etsa qeto ka palo ea li-layers marang-rang, palo ea li-neurone sebakeng se seng le se seng, le mofuta oa tsebetso ea tsebetso e tla sebelisoa karolong e ngoe le e ngoe. 3. Ho qala litekanyo: Mohato o latelang ke ho qala litekanyo tsa li-neurone tsa marang-rang. Hangata sena se etsoa ka mokhoa o sa reroang qalong ea thupelo, ka sepheo sa ho fumana boima bo nepahetseng ka mokhoa oa ho pheta-pheta oa ho ithuta. 4. Koetlisa mohlala: Hang ha litekanyo li se li qalisoa, mohlala o ka koetlisoa ka data ho sebelisa algorithm e kang backpropagation. Nakong ea koetliso, litekanyo tsa li-neurone lia fetoloa ha mohlala o ithuta ho data. Hangata sena se kenyelletsa ho pheta-pheta lintlha ka makhetlo a mangata le ho ntlafatsa litekanyo ha mohlala o etsa likhakanyo le ho amohela maikutlo mabapi le tsebetso ea oona. 5. Ho hlahloba mohlala: Ka mora hore mohlala o koetlisoe, u lokela ho hlahloba tsebetso ea oona ho dataset ea netefatso. Sena se tla u thusa ho lekola hore na mohlala o ka sebetsa hantle hakae ho data e ncha, e sa bonahaleng. 6. Ho lokisa mohlala hantle: Ho ipapisitse le liphetho tsa tlhahlobo, u ka etsa qeto ea ho lokisa mohlala ka ho lokisa meralo, sekhahla sa ho ithuta kapa li-hyperparameter tse ling. Sena se ka thusa ho ntlafatsa tsebetso ea mohlala ho dataset ea netefatso. 7. Ho bolela esale pele: Ka mora hore mohlala o koetlisoe le ho lokisoa hantle, o ka sebelisoa ho etsa likhakanyo ka lintlha tse ncha, tse sa bonahaleng. U ka sebelisa mohlala o koetlisitsoeng ho etsa likhakanyo bakeng sa mehlala e le ngoe kapa ho etsa likhakanyo ka bongata ba data. Ka kakaretso, ho theha mohlala oa marang-rang oa neural ho kenyelletsa ho hlalosa bothata, ho lokisa lintlha, ho khetholla mohaho, ho qala litekanyo, ho koetlisa mohlala, ho hlahloba tsebetso ea eona, ho lokisa mohlala hantle, me qetellong ho bolela esale pele ka mohlala o koetlisitsoeng. 