You're reading from Deep Learning for Beginners

Product typeBook

Published inSep 2020

Reading LevelBeginner

PublisherPackt

ISBN-139781838640859

Edition1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Author (1)

Dr. Pablo Rivas

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 brain. Psychological 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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Author (1)

Dr. Pablo Rivas

Dr. Pablo Rivas is an assistant professor of computer science at Baylor University in Texas. He worked in industry for a decade as a software engineer before becoming an academic. He is a senior member of the IEEE, ACM, and SIAM. He was formerly at NASA Goddard Space Flight Center performing research. He is an ally of women in technology, a deep learning evangelist, machine learning ethicist, and a proponent of the democratization of machine learning and artificial intelligence in general. He teaches machine learning and deep learning. Dr. Rivas is a published author and all his papers are related to machine learning, computer vision, and machine learning ethics. Dr. Rivas prefers Vim to Emacs and spaces to tabs.
Read more about Dr. Pablo Rivas

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