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. PacktPublishing Ltd.
Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1985). Learning internal representations by errorpropagation (No. ICS-8506). California Univ San Diego La Jolla Inst for Cognitive Science.