Popular gradient-based strategies for optimizing parameters in neural networks.
For a discussion regarding the generalization performance of the solutions found via different optimization strategies, see:
| [1] | Wilson et al. (2017) "The marginal value of adaptive gradient methods in machine learning", Proceedings of the 31st Conference on Neural Information Processing Systemshttps://arxiv.org/pdf/1705.08292.pdf |
.. autoclass:: numpy_ml.neural_nets.optimizers.optimizers.OptimizerBase :members: :undoc-members: :show-inheritance:
.. autoclass:: numpy_ml.neural_nets.optimizers.SGD :members: :undoc-members: :show-inheritance:
.. autoclass:: numpy_ml.neural_nets.optimizers.AdaGrad :members: :undoc-members: :show-inheritance:
.. autoclass:: numpy_ml.neural_nets.optimizers.Adam :members: :undoc-members: :show-inheritance:
.. autoclass:: numpy_ml.neural_nets.optimizers.RMSProp :members: :undoc-members: :show-inheritance:
