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Gradient descent is an optimization algorithm widely used in machine learning and statistics to minimize a function by iteratively moving towards the steepest descent direction, which is defined by the negative of the gradient. The primary goal is to update the parameters of a model, such as coefficients in linear regression or weights in neural networks, to reduce the cost function effectively.

The process begins at a starting point, often at a high cost, and takes steps in the direction indicated by the negative gradient. Each step is determined by the learning rate, which controls how large each update is. If the learning rate is too large, it can lead to divergence, while a small learning rate may result in slow convergence. Understanding gradient descent is crucial for grasping more advanced optimization techniques, such as stochastic gradient descent, which is commonly used in deep learning applications ²³⁴.

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Gradient Descent

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Gradient Descent Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In ...

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