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Machine learning tutorial: Selected bibliography

  

Neural Network Grey
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.

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Using the selected bibliography

This bibliography is a starting point for studying neural network foundations, architectures, learning algorithms and applications. Follow the original publication for the complete method, assumptions and experimental evidence; bibliographic entries should not be treated as substitutes for the source material.

When comparing methods, record the data set, preprocessing, validation design, metric and software version. These details are necessary to distinguish a reproducible comparison from a result that applies only to one experimental setup. For an applied sequence, begin with the machine learning tutorials and use this page to locate deeper references.