gradient descent

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gradient descent

Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of the steepest descent, which is defined by the negative of the gradient. In the context of machine learning, gradient descent is employed to update the parameters of models, such as coefficients in linear regression or weights in neural networks. The process involves starting at a point (often a high-cost area) and taking steps downhill towards a lower-cost area, guided by the negative gradient at each point ².

The algorithm operates by calculating the gradient of the cost function at the current point, which indicates the direction of steepest ascent. By moving in the opposite direction, the algorithm aims to reduce the cost function. The size of the steps taken is determined by a parameter known as the learning rate. If the learning rate is too large, it can lead to divergence, while a small learning rate may result in slow convergence ³⁴.

Overall, understanding gradient descent is crucial for grasping more advanced optimization techniques used in machine learning and deep learning ³.

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