Adam stochastic gradient descent optimization

`fmin_adam` is an implementation of the Adam optimisation algorithm (gradient descent with Adaptive learning rates individually on each parameter, with Momentum) from Kingma and Ba [1]. Adam is designed to work on stochastic gradient descent problems; i.e. when only small batches of data are used to estimate the gradient on each iteration, or when stochastic dropout regularisation is used [2].
See GIT repository for examples:
https://github.com/DylanMuir/fmin_adam

Usage:
[x, fval, exitflag, output] = fmin_adam(fun, x0 <, stepSize, beta1, beta2, epsilon, nEpochSize, options>)

See the function help for a detailed reference. The github repository has a couple of examples.

References:
[1] Diederik P. Kingma, Jimmy Ba. "Adam: A Method for Stochastic Optimization", ICLR 2015. [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)

[2] Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R. Salakhutdinov. "Improving neural networks by preventing co-adaptation of feature detectors." arXiv preprint. [https://arxiv.org/abs/1207.0580](https://arxiv.org/abs/1207.0580)