<prompt> Hlalosa hore na Back Propagation ke eng le hore na e sebetsa joang.nan  <response>: Instruction in English: Explain what Back Propagation is and how it works. . Response in English: Backpropagation is a method used in artificial neural networks to calculate the error contribution of each neuron after a batch of data in image recognition or other data analyses is processed. Its purpose is to minimize the cost or loss function by adjusting the weights and biases of the neural network, which ultimately improves the accuracy of the model.Backpropagation is short for backward propagation of errors. It is a supervised learning algorithm that is based on the chain rule of calculus to compute gradients. During training, an input is propagated forward through the network to obtain an output, then the output is compared to the desired output, and the error is calculated. The error is then propagated backwards through the network, and the gradient of the cost function with respect to the networks weights is computed. Finally, the weights are updated according to an optimization algorithm such as gradient descent.The process is repeated for many epochs complete passes through the training set until the model is able to make accurate predictions. The objective is to have the model make predictions that are as close as possible to the actual desired outputs. The models weights are adjusted in a way that minimizes the error between the desired outputs and predicted outputs.In summary, backpropagation is an algorithm used to train neural networks by minimizing the error between the predicted and the actual output. It does this by computing the gradient of the loss function with respect to the weights, then updating the weights to reduce the error. This process is repeated until the error is minimized to an acceptable level. . Response in Sesotho: Backpropagation ke mokhoa o sebelisoang ho marang-rang a maiketsetso a methapo ea kutlo ho bala monehelo oa phoso oa neuron e ngoe le e ngoe ka mora hore sehlopha sa data ka ho lemoha litšoantšo kapa litlhahlobo tse ling tsa data se sebetsoe. Sepheo sa eona ke ho fokotsa tsebetso ea litšenyehelo kapa tahlehelo ka ho lokisa litekanyo le litšekamelo tsa marang-rang a marang-rang, e leng ho ntlafatsang ho nepahala ha mohlala. Phatlalatso ea morao e khutsufetse bakeng sa khatiso ea morao ea liphoso. Ke algorithm ea ho ithuta e hlokometsoeng e ipapisitseng le melaoana ea calculus ea ho bala li-gradients. Nakong ea koetliso, ho kenya letsoho ho phatlalatsoa pele ka marang-rang ho fumana sehlahisoa, joale sehlahisoa se bapisoa le sehlahisoa se lakatsehang, me phoso e baloa. Phoso e phatlalalitsoe morao ka marang-rang, me gradient ea tsebetso ea litšenyehelo mabapi le boima ba marang-rang e baloa. Qetellong, boima bo ntlafatsoa ho latela algorithm ea optimization joalo ka ho theoha ha gradient. Tsebetso e phetoa bakeng sa li-epochs tse ngata e felletseng e feta ka hara sete ea koetliso ho fihlela mohlala o khona ho etsa likhakanyo tse nepahetseng. Sepheo ke ho etsa hore mohlala o etse likhakanyo tse haufi haholo le liphetho tse lakatsehang. Boima ba mohlala bo lokisoa ka tsela e fokotsang phoso pakeng tsa liphetho tse lakatsehang le tse boletsoeng esale pele. Ka kakaretso, tlhahiso ea morao-rao ke algorithm e sebelisetsoang ho koetlisa marang-rang a neural ka ho fokotsa phoso pakeng tsa se boletsoeng esale pele le tlhahiso ea sebele. E etsa sena ka ho kopanya gradient ea tsebetso ea tahlehelo mabapi le litekanyo, ebe e nchafatsa litekanyo ho fokotsa phoso. Tsebetso ena e phetoa ho fihlela phoso e fokotsoa ho isa boemong bo amohelehang. 