batch gradient descent
Batch gradient descent is an optimization algorithm used for finding the optimal solution to a machine learning problem. It iteratively updates the model's parameters by calculating the gradient of the loss function based on the entire training dataset. This method is computationally expensive but guarantees convergence to the global minimum.
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Similar Concepts
- accelerated gradient descent methods
- adaptive learning rates in gradient descent
- conjugate gradient descent
- constraints in gradient descent optimization
- convergence of gradient descent
- 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
- stochastic gradient descent
- variants of gradient descent algorithms