<prompt> Hlalosa mohopolo oa ho theoha ha likhahla.nan  <response>: Instruction in English: Explain the concept of gradient descent. . Response in English: Gradient descent is an optimization algorithm for finding the minimum of a function. It is commonly used in machine learning to minimize the cost function associated with a given model, such as linear regression, logistic regression, and neural networks, in order to improve the accuracy of the models predictions.The fundamental idea behind gradient descent is to iteratively adjust the values of the parameters or weights of the model in the direction of the steepest descent of the cost function. The gradient of the cost function with respect to the parameters gives us the direction of the steepest descent, and the magnitude of the gradient tells us how steep the slope is.At each iteration, the parameters are updated by taking a step in the direction of the negative gradient. The step size, or learning rate, determines how large of a step we take along the gradient, and it is an important hyperparameter that can affect the performance of the algorithm.There are several variations of gradient descent, including batch gradient descent, stochastic gradient descent, and mini-batch gradient descent, which differ in how the gradient is calculated and how the parameters are updated.In summary, gradient descent is an iterative optimization algorithm that allows us to find the optimal values of the parameters of a model by following the gradient of the cost function, making adjustments to the parameters at each iteration to move closer and closer to the minimum of the cost function. . Response in Sesotho: Ho theoha ha Gradient ke algorithm ea optimization bakeng sa ho fumana bonyane ba tšebetso. E sebelisoa hangata thutong ea mochini ho fokotsa tsebetso ea litjeo e amanang le mofuta o fanoeng, joalo ka ho fokotseha ha linear, logistic regression, le neural network, molemong oa ho ntlafatsa ho nepahala ha likhakanyo tsa mohlala. Mohopolo oa mantlha ka mora ho theoha ha gradient ke ho fetola khafetsa boleng ba liparamente kapa litekanyo tsa mohlala ho ea moo ho theohang ho hoholo ha theko ea tšebetso. Boemo ba tsebetso ea litšenyehelo mabapi le liparamente bo re fa tsela ea ho theoha ho fetisisa, me boholo ba gradient bo re bolella hore na moepa o moepa hakae. Ka nako e ngoe le e ngoe e pheta-phetoang, li-parameter li nchafatsoa ka ho nka mohato ho ea ho gradient e mpe. Boholo ba mohato, kapa sekhahla sa ho ithuta, se etsa qeto ea hore na re nka bohato bo bokae ho latela gradient, me ke hyperparameter ea bohlokoa e ka amang tsebetso ea algorithm. Ho na le liphapano tse maloa tsa ho theoha ha sekhahla, ho kenyelletsa le ho theoha ha sehlopha, ho theoha ha sekhahla sa stochastic, le ho theoha ha mini-batch gradient, tse fapaneng ka tsela eo gradient e baloang ka eona le hore na liparamente li nchafatsoa joang. Ka bokhutšoanyane, ho theoha ha gradient ke algorithm e iterative optimization e re lumellang ho fumana boleng bo nepahetseng ba liparamente tsa mohlala ka ho latela gradient ea tsebetso ea litšenyehelo, ho etsa litokiso ho li-parameter nako le nako ho atamela haufi le bonyane ba mosebetsi oa litšenyehelo. 