Optimizers
===========
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 Systems*
   https://arxiv.org/pdf/1705.08292.pdf

``OptimizerBase``
-------------
.. autoclass:: numpy_ml.neural_nets.optimizers.optimizers.OptimizerBase
    :members:
    :undoc-members:
    :show-inheritance:

``SGD``
-----------
.. autoclass:: numpy_ml.neural_nets.optimizers.SGD
    :members:
    :undoc-members:
    :show-inheritance:

``AdaGrad``
-----------
.. autoclass:: numpy_ml.neural_nets.optimizers.AdaGrad
    :members:
    :undoc-members:
    :show-inheritance:

``Adam``
-----------
.. autoclass:: numpy_ml.neural_nets.optimizers.Adam
    :members:
    :undoc-members:
    :show-inheritance:

``RMSProp``
-----------
.. autoclass:: numpy_ml.neural_nets.optimizers.RMSProp
    :members:
    :undoc-members:
    :show-inheritance: