<prompt> Akaretsa tsebetso ea hore na marang-rang a neural a ho ithuta a tebileng a sebetsa joang.nan  <response>: Instruction in English: Summarize the process of how a deep learning neural network works. . Response in English: A deep learning neural network is a type of artificial intelligence algorithm modeled after the structures and functions of the human brain. It consists of interconnected nodes, called neurons or units, arranged in layers.The process of how a deep learning neural network works can be summarized in the following steps:1. Data input: The neural network receives input data, which is then fed into the first layer of neurons. Each neuron in this layer processes the data in some way, typically by applying a mathematical function.2. Data propagation: The output from the first layer of neurons is then passed along to the next layer, where the process is repeated. This continues until the data reaches the final output layer.3. Data analysis: The final output from the neural network is then analyzed to see how well it matches the desired result. If the result is not satisfactory, the algorithm adjusts the weights and biases of the neurons in an attempt to improve the accuracy of the output.4. Learning: Through a process called backpropagation, the algorithm calculates the error between the desired output and the actual output, and makes adjustments to the weights and biases of the neurons to minimize this error. This continues iteratively until the algorithm converges on the best possible solution.In summary, a deep learning neural network takes input data, processes it through multiple layers of interconnected neurons, and produces an output. This output is then compared to the desired result, and the algorithm is adjusted and fine-tuned through backpropagation to improve its accuracy. . Response in Sesotho: Neural network e tebileng ea ho ithuta ke mofuta oa algorithm ea bohlale ba maiketsetso e entsoeng ho latela libopeho le mesebetsi ea boko ba motho. E na le li-node tse hokahaneng, tse bitsoang li-neurone kapa li-unit, tse hlophisitsoeng ka mekhahlelo. Tsebetso ea hore na marang-rang a neural a ho ithuta ka botebo a sebetsa joang a ka akaretsoa ka mehato e latelang: 1. Ho kenya data: Neural network e fumana data e kenang, ebe e kenngoa karolong ea pele ea methapo. Neurone e ngoe le e ngoe e karolong ena e sebetsana le data ka tsela e itseng, hangata ka ho sebelisa mosebetsi oa lipalo. 2. Phatlalatso ea data: Tlhahiso e tsoang karolong ea pele ea li-neurone e ntano fetisetsoa lera le latelang, moo tsebetso e phetoang. Sena se tsoela pele ho fihlela data e fihla lera la ho qetela la tlhahiso. 3. Tlhahlobo ea lintlha: Tlhahiso ea ho qetela e tsoang ho neural network e ntano hlahlojoa ho bona hore na e lumellana hantle hakae le sephetho se lakatsehang. Haeba sephetho se sa khotsofatse, algorithm e lokisa boima le leeme la li-neurone ka boiteko ba ho ntlafatsa ho nepahala ha tlhahiso. 4. Ho Ithuta: Ka mokhoa o bitsoang backpropagation, algorithm e lekanya phoso pakeng tsa tlhahiso e lakatsehang le tlhahiso ea sebele, me e etsa litokiso ho boima le leeme la li-neurons ho fokotsa phoso ena. Sena se tsoela pele khafetsa ho fihlela algorithm e kopana ka tharollo e molemohali e ka khonehang. Ka kakaretso, marang-rang a neural a ho ithuta ka botebo a nka data e kenang, e e sebetsa ka mekhahlelo e mengata ea li-neurone tse hokahaneng, me e hlahisa tlhahiso. Sehlahisoa sena se bapisoa le sephetho se lakatsehang, me algorithm ea lokisoa le ho hlophisoa hantle ka li-backpropagation ho ntlafatsa ho nepahala ha eona. 