<prompt> Algorithm ea backprop e sebelisoa joang thutong e tebileng?nan  <response>: Instruction in English: How is a backprop algorithm used in deep learning? . Response in English: In deep learning, a backpropagation algorithm is used to train neural networks by efficiently calculating the gradient of the loss function with respect to the networks weights. The backpropagation algorithm consists of two main steps: the forward pass and the backward pass. In the forward pass, the input data is passed through the network to produce an output, which is then compared to the desired output to calculate the loss. In the backward pass, the error is propagated backwards through the network, starting from the output layer and moving towards the input layer. The error is used to compute the gradients of the loss with respect to each weight in the network. The gradients are calculated by applying the chain rule of calculus to the computational graph of the network. Once the gradients are calculated, they can be used to update the weights in the network through an optimization algorithm such as stochastic gradient descent. The weights are updated in such a manner that the loss function is minimized, effectively enabling the neural network to learn from its errors.The backpropagation algorithm is a powerful tool in deep learning, as it enables large neural networks to be trained efficiently. It has become the standard method for training neural networks, and has been widely used in various applications, including image recognition, speech recognition, natural language processing and more. . Response in Sesotho: Thutong e tebileng, algorithm ea backpropagation e sebelisoa ho koetlisa marang-rang a marang-rang ka ho bala ka nepo sekhahla sa tsebetso ea tahlehelo mabapi le boima ba marang-rang. The backpropagation algorithm e na le mehato e meli e meholo: ho fetela pele le ho khutlela morao. Phaleng ea pele, data ea ho kenya e fetisoa ka marang-rang ho hlahisa tlhahiso, e ntano bapisoa le sehlahisoa se lakatsehang ho bala tahlehelo. Phalong e ka morao, phoso e phatlalatsoa ka morao ka marang-rang, ho qala ho tloha lera la tlhahiso le ho ea ho lera la ho kenya. Phoso e sebelisetsoa ho bala li-gradients tsa tahlehelo mabapi le boima bo bong le bo bong ba marang-rang. Li-gradients li baloa ka ho sebelisa molao oa ketane ea calculus ho graph ea computational ea marang-rang. Hang ha li-gradient li baloa, li ka sebelisoa ho nchafatsa litekanyo tsa marang-rang ka algorithm ea optimization joalo ka stochastic gradient descent. Boima bo ntlafatsoa ka mokhoa oo tsebetso ea tahlehelo e fokotsoang, e leng ho nolofalletsang neural network ho ithuta liphosong tsa eona. Backpropagation algorithm ke sesebelisoa se matla thutong e tebileng, kaha e nolofalletsa marang-rang a maholo a methapo ea kutlo ho koetlisoa hantle. E se e le mokhoa o tloaelehileng oa ho koetlisa marang-rang a methapo ea kutlo, me e sebelisitsoe haholo litsebetsong tse fapaneng, ho kenyeletsoa temoho ea litšoantšo, temoho ea puo, tsebetso ea puo ea tlhaho le tse ling. 