gradient descent
Gradient descent is an optimization algorithm used to find the optimal solution for a mathematical function by iteratively adjusting its parameters in the opposite direction of the gradient, or the steepest descent, until reaching the minimum.
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Related Concepts (22)
- accelerated gradient descent methods
- adam optimization algorithm
- adaptive learning rates in gradient descent
- backpropagation
- batch gradient descent
- conjugate gradient descent
- constraints in gradient descent optimization
- convergence of gradient descent
- empirical risk minimization in gradient descent
- fully connected layers
- gradient descent for linear regression
- gradient descent for neural networks
- hybrid optimization algorithms combining gradient descent
- mini-batch gradient descent
- non-convex optimization using gradient descent
- online gradient descent
- parallel and distributed gradient descent
- proximal gradient descent
- regularization techniques in gradient descent
- second-order methods in gradient descent
- stochastic gradient descent
- variants of gradient descent algorithms
Similar Concepts
- adaptive optimization
- genetic algorithm
- genetic algorithms
- gradient index
- grid integration
- grid optimization
- learning algorithms
- nonlinear optimization
- optimization algorithms
- pressure gradient
- pressure gradients
- steep learning curve
- stochastic optimization
- training algorithms
- vanishing gradient problem