Adam Optimizer
Machine Learning
An advanced optimization algorithm that combines the benefits of RMSprop and momentum optimization for training neural networks.
Detailed Explanation
Adam Optimizer is an advanced algorithm used to train neural networks by adaptively adjusting learning rates for each parameter. It combines the benefits of RMSprop’s ability to handle sparse gradients and momentum’s acceleration of convergence. This results in faster training, better convergence stability, and improved overall performance in deep learning models.
Use Cases
- •Use case: Accelerate neural network training for image recognition tasks with faster convergence and improved stability using Adam Optimizer.
Related Terms
Other terms in the Machine Learning category