<prompt> Hlalosa mohopolo oa li-network tsa neural tsa maiketsetso.nan  <response>: Instruction in English: Explain the concept of artificial neural networks. . Response in English: An artificial neural network is a computational model inspired by the structure and function of the biological neural network of the human brain. It is used in machine learning and artificial intelligence to predict outcomes, recognize patterns, and classify data. A neural network consists of layers of interconnected nodes or neurons that are able to process information. Each neuron receives input from other neurons in the previous layer, processes the information using a set of mathematical operations, and then sends the output to neurons in the next layer. The first layer in a neural network is the input layer, where data is fed into the network for processing. The last layer is the output layer, where the final results are obtained. In between, there may be one or more hidden layers that perform complex transformations on the data. The connections between the neurons in different layers have associated weights, which determine how much influence one neuron has on another.Neural networks are trained using a data set with known outcomes, in a process called supervised learning. During the training process, the weights of the connections between neurons are adjusted to minimize the difference between the predicted and actual outcomes. Once the network is trained, it can be used to make predictions on new data.Artificial neural networks have a wide range of applications, including image and speech recognition, natural language processing, and predictive modeling. They are particularly useful in solving problems where the relationships between data points are complex and difficult to model using traditional methods. . Response in Sesotho: Artificial neural network ke mohlala oa computational o susumetsoang ke sebopeho le tšebetso ea netweke ea bioloji ea boko ba motho. E sebelisoa thutong ea mochini le bohlaleng ba maiketsetso ho bolela esale pele liphetho, ho lemoha lipaterone le ho hlophisa lintlha. Neural network e na le likarolo tsa li-node tse hokahaneng kapa neuron tse khonang ho sebetsana le tlhahisoleseling. Neuron e ngoe le e ngoe e amohela tlhahiso ho tsoa ho li-neuron tse ling tse karolong e fetileng, e sebetsana le tlhahisoleseling e sebelisa sete ea tsebetso ea lipalo, ebe e romela tlhahiso ho li-neurone tse karolong e latelang. Lera la pele la marang-rang a neural ke lesela la ho kenya, moo data e feptjoang marang-rang bakeng sa ho sebetsa. Lera la ho qetela ke lera la tlhahiso, moo liphetho tsa ho qetela li fumanoang. Hara lipakeng, ho ka na ha e-ba le lihlopha tse patiloeng kapa tse ngata tse etsang liphetoho tse rarahaneng ho data. Likamano pakeng tsa li-neurone tse ka hara mekhahlelo e fapaneng li na le boima bo amanang le tsona, tse bontšang hore na neuron e ngoe e na le tšusumetso e kae ho e ngoe. Marang-rang a Neural a koetlisoa ka ho sebelisa sete ea data e nang le liphetho tse tsebahalang, ka mokhoa o bitsoang thuto e hlokometsoeng. Nakong ea thupelo, boima ba likhokahano lipakeng tsa li-neurone lia fetoloa ho fokotsa phapang lipakeng tsa liphetho tse boletsoeng esale pele le tsa nete. Hang ha marang-rang a koetlisoa, a ka sebelisoa ho etsa likhakanyo ka lintlha tse ncha. Marang-rang a maiketsetso a neural a na le mefuta e mengata ea tsebeliso, ho kenyeletsoa temoho ea litšoantšo le puo, tsebetso ea puo ea tlhaho, le mohlala oa pele. Li na le thuso ka ho khetheha ho rarolla mathata moo likamano pakeng tsa lintlha tsa data li rarahaneng le ho thata ho etsa mohlala ho sebelisa mekhoa ea setso. 