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References

  • Hecht-Nielsen, R. (1992). Theory of the backpropagation neural network. In Neural networks for perception (pp. 65-93). Academic Press.
  • Kane, F. (2017). Hands-On Data Science and Python ML. Packt Publishing Ltd.
  • LeCun, Y., Bottou, L., Orr, G., and Muller, K. (1998). Efficient backprop in neural networks: Tricks of the trade (Orr, G. and Müller, K., eds.). Lecture Notes in Computer Science, 1524(98), 111.
  • Ojeda, T., Murphy, S. P., Bengfort, B., and Dasgupta, A. (2014). Practical Data Science Cookbook. Packt Publishing Ltd.
  • Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brainPsychological Review, 65(6), 386.
  • Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1985). Learning internal representations by error propagation (No. ICS-8506). California Univ San Diego La Jolla Inst for Cognitive Science.
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