<prompt> Ngola tlhaloso ea meralo ea mohlala oa neural network e tebileng.nan  <response>: Instruction in English: Write a description of the architecture of a deep neural network model. . Response in English: A deep neural network DNN is a type of artificial neural network that consists of multiple layers of interconnected nodes that allow the model to learn complex data patterns. The architecture of a DNN typically includes an input layer, multiple hidden layers, and an output layer. Each layer is composed of multiple nodes or neurons that process information and pass it on to the next layer.The input layer receives raw data in the form of features and passes it to the first hidden layer. Each hidden layer applies a non-linear transformation to the data using weights and bias obtained during the training process. The weights and biases are adjusted as the model learns from the data, to minimize the error between the predicted and actual output. The hidden layers help to extract increasingly high-level features and patterns from the data as it passes through the network.The output layer provides the final prediction or classification based on the aggregated information from previous layers. The number of nodes in the output layer depends on the task at hand. For example, in a classification task, the number of nodes would correspond to the number of classes in the dataset.The architecture of a DNN can vary depending on the specific task and design choices. Factors such as the depth of the network, the number of nodes in each layer, and the type of activation functions used can all affect the performance of the network. . Response in Sesotho: deep neural network DNN ke mofuta oa marang-rang a maiketsetso a maiketsetso a nang le likarolo tse ngata tsa li-node tse hokahaneng tse lumellang mohlala ho ithuta mekhoa e rarahaneng ea data. Mehaho ea DNN hangata e kenyelletsa lera la ho kenya, likarolo tse ngata tse patiloeng, le karolo e hlahisoang. Lera le leng le le leng le entsoe ka li-node tse ngata kapa neurone tse sebetsanang le tlhahisoleseling ebe li e fetisetsa karolong e latelang. Sekhahla sa ho kenya se fumana data e tala ka mokhoa oa likarolo ebe se e fetisetsa karolong ea pele e patiloeng. Lera le leng le le leng le patiloeng le sebetsa phetoho e se nang moeli ho data ho sebelisa boima le leeme le fumanoang nakong ea koetliso. Boima le leeme lia fetoloa ha mohlala o ntse o ithuta ho data, ho fokotsa phoso lipakeng tsa sephetho se boletsoeng esale pele le sa nete. Likarolo tse patiloeng li thusa ho ntša likarolo tse ntseng li eketseha tsa boemo bo phahameng le mekhoa e tsoang ho data ha e feta marang-rang. Lera la tlhahiso le fana ka khakanyo ea ho qetela kapa lihlopha tse ipapisitseng le lintlha tse kopantsoeng ho tsoa ho mekhahlelo e fetileng. Palo ea li-node tsa lera la tlhahiso e itšetlehile ka mosebetsi o etsoang. Ka mohlala, mosebetsing oa ho hlophisa, palo ea li-node e tla lumellana le palo ea lihlopha ho dataset. Mehaho ea DNN e ka fapana ho latela mosebetsi o itseng le khetho ea moralo. Lintlha tse kang botebo ba marang-rang, palo ea li-node karolong e ngoe le e ngoe, le mofuta oa mesebetsi ea tsebetso e sebelisoang kaofela e ka ama tsebetso ea marang-rang. 