Graph Neural Networks
Deep Learning
An optimization algorithm that iteratively adjusts parameters to minimize a cost function by moving in the direction of steepest descent.
Detailed Explanation
Gradient Descent is a fundamental optimization algorithm in deep learning that adjusts model parameters to minimize the cost function. It works by calculating the gradient (slope) of the loss with respect to parameters and updating them in the opposite direction of the gradient, iteratively reducing error and improving model accuracy. Variants include batch, stochastic, and mini-batch gradient descent.
Use Cases
- •Training neural networks efficiently by updating weights iteratively to reduce error and improve predictions.
Related Terms
Other terms in the Deep Learning category