<prompt> Algorithm ea ho theoha ha gradient ke eng?nan  <response>: Instruction in English: What is the gradient descent algorithm? . Response in English: Gradient Descent is an optimization algorithm commonly used to find the optimal values of parameters of machine learning models such as coefficients in regression analysis or weights in neural network. The algorithm works by iteratively adjusting the values of these parameters in order to find the minimum of a cost function, which is a measure of how poorly our model is performing.The gradient descent algorithm begins by initializing the parameters with a random value. Then, in each iteration, the algorithm computes the gradient of the cost function with respect to the parameters. The gradient gives us the direction in which the cost function is increasing most rapidly. We then take a step in the opposite direction by subtracting the computed gradient from the current values of the parameters, effectively moving towards the minima in each iteration.The size of the step we take is determined by the learning rate, a hyperparameter that controls how fast the model learns. A smaller learning rate means that we take smaller steps and may need more iterations to converge to the optima, while a larger learning rate means that we take larger steps, and may converge faster or overshoot and fail to converge at all.Gradient descent can be applied in many variations, including batch gradient descent, stochastic gradient descent, and mini-batch gradient descent, where the algorithm processes the training examples in different manners to update the parameters. . Response in Sesotho: Gradient Descent ke algorithm ea optimization eo hangata e sebelisoang ho fumana boleng bo nepahetseng ba liparamente tsa mefuta ea ho ithuta ea mochini joalo ka li-coefficients tlhahlobong ea regression kapa boima ho neural network. Algorithm e sebetsa ka ho pheta-pheta litekanyetso tsa litekanyetso tsena e le ho fumana bonyane ba tsebetso ea litšenyehelo, e leng tekanyo ea hore na mohlala oa rona o sebetsa hampe hakae. Algorithm ea ho theoha ha gradient e qala ka ho qala liparamente ka boleng bo sa reroang. Joale, ka nako e ngoe le e ngoe ea ho pheta-pheta, algorithm e lekanya lebelo la tsebetso ea litšenyehelo mabapi le li-parameter. Gradient e re fa tataiso eo tsebetso ea litšenyehelo e ntseng e eketseha ka potlako. Ka mora moo re nka mohato o fapaneng ka ho tlosa gradient e entsoeng ka khomphieutha ho tloha ho boleng ba hona joale ba li-parameter, ka katleho ho ea ho minima ka nako e ngoe le e ngoe ea ho pheta-pheta. Boholo ba mohato oo re o nkang bo khethoa ke sekhahla sa ho ithuta, hyperparameter e laolang hore na mohlala o ithuta ka potlako hakae. Sekhahla se senyane sa ho ithuta se bolela hore re nka mehato e menyenyane mme re ka hloka ho pheta-pheta ho feta ho kopana ho fihlela sephetho se nepahetseng, athe sekhahla se seholo sa ho ithuta se bolela hore re nka mehato e meholoanyane, me re ka kopana ka potlako kapa ra feta, ra hloleha ho kopana ho hang. Ho theoha ha likhahla ho ka sebelisoa ka mefuta e mengata e fapaneng, ho kenyeletsoa ho theoha ha sehlopha, ho theoha ha stochastic gradient, le ho theoha ha mini-batch gradient, moo algorithm e sebetsanang le mehlala ea koetliso ka mekhoa e fapaneng ho nchafatsa liparamente. 