Fundamentals of recurrent neural network (RNN) and long-short term memory (LSTM) network.
A Sherstinsky. Physica D: Nonlinear Phenomena, 404, 2020.Neural networks and deep learning. CC Aggarwal. Springer, 2018.

Adam: A method for stochastic optimization. Diederik P Kingma and Jimmy Ba. arXiv preprint arXiv:1412.6980, 2014.

Learning precise timing with lstm recurrent networks. FA Gers, NN Schraudolph and J Schmidhuber. Journal of Machine Learning Research, 3(1):115-143, 2003.

Neural networks for pattern recognition. CM Bishop. Oxford university press, 1995.

Training feedforward networks with the Marquardt algorithm. MT Hagan and M Menhaj. IEEE Transactions on Neural Networks, vol. 5, no. 6, pp. 989–993, 1994.

First and second order methods for learning: Between steepest descent and Newton’s method. R Battiti. Neural Computation, vol. 4, no. 2, pp. 141–166, 1992.

Multilayer feedforward networks are universal approximators. K Hornik, M Stinchcombe and H White. Neural Networks, 2(5):359-366, 1989.

Learning representations by back-propagating errors. DE Rumelhart, GE Hinton and RJ Williams. Nature, vol. 323, pp. 533–536, 1986.

Function minimization by conjugate gradients. R Fletcher and CM Reeves Computer Journal, vol. 7, pp. 149-154, 1964.

Principles of Neurodynamics. F Rosenblatt. Washington D.C.: Spartan Press, 1961.

The Organization of Behavior. DO Hebb. New York: Wiley, 1949.

A logical calculus of ideas immanent in nervous activity. WS McCulloch and WH Pitts. Bulletin of Mathematical Biophysics, vol. 5, pp. 115-133, 1943.

⇐ Model Deployment