Was ist Gradient Descent

Definition

Gradient Descent is an optimization algorithm used to minimize a model's error function (or 'loss function') by iteratively adjusting the model's parameters in the direction of the steepest descent.

How It Works

The algorithm calculates the gradient (the mathematical derivative) of the error function with respect to the neural network's current weights. Subsequently, these weights are adjusted in the opposite direction of the gradient to move closer to the global or local minimum of the error function. The magnitude of these adjustments is controlled by the 'learning rate'. In the context of SaaS and deep learning, Gradient Descent is the mathematical workhorse powering the training of nearly all AI models. Without this efficient optimization method, training complex models for CRM analytics, sales forecasting, or natural language processing would be impractical within acceptable timeframes and with moderate hardware resources.