gradient descent for neural networks
Gradient descent for neural networks is an optimization algorithm that adjusts the weights and biases of a neural network by repeatedly computing the gradient of the loss function with respect to the network parameters and updating them in the opposite direction of the gradient to minimize the loss and improve the network's accuracy during training.
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Similar Concepts
- accelerated gradient descent methods
- batch gradient descent
- conjugate gradient descent
- constraints in gradient descent optimization
- convergence of gradient descent
- gradient descent for linear regression
- hybrid optimization algorithms combining gradient descent
- mini-batch gradient descent
- neural network training
- non-convex optimization using gradient descent
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