<prompt> Hlalosa molao-motheo o ka sehloohong oa algorithm e fanoeng.Ho theoha ha Gradient  <response>: Instruction in English: Explain the main principle of the given algorithm. . Response in English: Gradient Descent is an optimization algorithm that is commonly used to train machine learning models. The main principle of the algorithm is to minimize a given function called the objective function by iteratively moving in the direction of the steepest descent, defined as the negative of the gradient.In the context of machine learning, the objective function is often the cost or loss function, which measures how well the model is performing. The goal of the algorithm is to find the parameters of the model that minimize the cost.To do this, the Gradient Descent algorithm starts with an initial set of parameters, and iteratively updates them by computing the gradient of the cost function with respect to the parameters and taking a step in the direction of the negative gradient. The size of the step is determined by the learning rate, a hyperparameter that is set by the user.The process is repeated until the algorithm converges to a minimum value of the cost function, or until a maximum number of iterations is reached. The final set of parameters represents the best solution found by the algorithm, and can be used to make predictions with the trained model.In summary, the main principle of the Gradient Descent algorithm is to iteratively update the parameters of a model by moving in the direction of the negative gradient of the cost function, with the goal of minimizing the cost and improving the performance of the model. . Response in Sesotho: Gradient Descent ke algorithm ea optimization eo hangata e sebelisoang ho koetlisa mefuta ea ho ithuta ka mochini. Molao-motheo o ka sehloohong oa algorithm ke ho fokotsa tsebetso e fanoeng e bitsoang sepheo sa sepheo ka ho tsamaea khafetsa ka lehlakoreng la moepa o phahameng ka ho fetisisa, o hlalosoang e le ntho e mpe ea gradient. Boemong ba ho ithuta ka mochini, sepheo sa sepheo hangata ke tsebetso ea litšenyehelo kapa tahlehelo, e lekanyang hore na mohlala o sebetsa hantle hakae. Sepheo sa algorithm ke ho fumana mekhahlelo ea mohlala e fokotsang litšenyehelo. Ho etsa sena, algorithm ea Gradient Descent e qala ka sete ea pele ea li-parameter, me e li nchafatsa khafetsa ka ho khomphutha lebelo la tsebetso ea litšenyehelo mabapi le liparamente le ho nka mohato ho leba ho gradient e mpe. Boholo ba mohato bo khethoa ke sekhahla sa ho ithuta, hyperparameter e behiloeng ke mosebedisi. Tsebetso e phetoa ho fihlela algorithm e fetohela ho boleng bo fokolang ba tsebetso ea litšenyehelo, kapa ho fihlela palo e kholo ea ho pheta-pheta e fihletsoe. Sehlopha sa ho qetela sa litekanyetso se emela tharollo e molemo ka ho fetisisa e fumanoang ke algorithm, me e ka sebelisoa ho etsa likhakanyo ka mohlala o koetlisitsoeng. Ka bokhutsoane, molao-motheo o ka sehloohong oa algorithm ea Gradient Descent ke ho nchafatsa maemo a mohlala ka makhetlo-khetlo ka ho ea ka lehlakoreng la gradient e mpe ea tsebetso ea litšenyehelo, ka sepheo sa ho fokotsa litšenyehelo le ho ntlafatsa tsebetso ea mohlala. 