<prompt> Theha Multilayer Perceptron MLP Neural Network e nang le lintho tse tharo tse kenang le tlhahiso e le ngoe e ka bolelang esale pele tlhahiso e thehiloeng ho mefuta e fapaneng ea ho kenya.nan  <response>: Instruction in English: Create a Multilayer Perceptron MLP Neural Network with three inputs and one output that can predict an output based on the input variables. . Response in English: Heres an example of how to create a Multilayer Perceptron MLP Neural Network with three inputs and one output using the Keras library in Python:pythonfrom keras.models import Sequentialfrom keras.layers import Dense Define the modelmodel Sequentialmodel.addDense12, input_dim3, activationrelumodel.addDense8, activationrelumodel.addDense1, activationsigmoid Compile the modelmodel.compilelossbinary_crossentropy, optimizeradam, metricsaccuracy Fit the model to the datax training inputsy training outputsmodel.fitx, y, epochs150, batch_size10 make predictions on new datapredictions model.predictnew_dataThis model has an input layer with 3 inputs, two hidden layers with 12 and 8 nodes, and an output layer with 1 output. The model uses the Rectified Linear Unit ReLU activation function for the hidden layers and the Sigmoid activation function for the output layer. The model is compiled with binary cross-entropy as the loss function and adam as the optimizer. The model is then fit to training data, and can be used to make predictions on new data. The number of epochs and batch size can be adjusted to improve the performance of the model. . Response in Sesotho: Mohlala ke ona oa mokhoa oa ho theha Multilayer Perceptron MLP Neural Network e nang le lisebelisoa tse tharo le tlhahiso e le ngoe u sebelisa laeborari ea Keras e Python: python ho tloha keras.models import Sequential from keras.layers import Dense Hlalosa mohlala oa mohlala Sequential model.addDense12, input_dim3, activationrelu model.addDense8, activationrelu model.eketsaTerene1, activationsigmoid Kopanya mohlala oa mohlala. kopanyatahlehelobinary_crossentropy, optimizeradam, metricsaccuracy Lokisa mohlala ho data x lisebelisoa tsa koetliso y liphetho tsa koetliso model.fit x, y, epochs150, batch_size10 etsa likhakanyo mabapi le likhakanyo tse ncha tsa data model.predictnew_data Moetso ona o na le lera la ho kenya le nang le lintlha tse 3, tse peli tse patiloeng tse nang le 12 le 8 node, le lera la tlhahiso le 1 tlhahiso. Mohlala o sebelisa tsebetso ea tsebetso ea Rectified Linear Unit ReLU bakeng sa likarolo tse patiloeng le tsebetso ea tsebetso ea Sigmoid bakeng sa sekhahla sa tlhahiso. Mohlala o hlophisitsoe ka binary cross-entropy e le tsebetso ea tahlehelo le adam joalo ka optimizer. Joale mohlala o loketse data ea koetliso, me o ka sebelisoa ho bolela esale pele ka data e ncha. Palo ea li-epoch le boholo ba batch e ka fetoloa ho ntlafatsa tsebetso ea mohlala. 