Artificial Neural Networks

Artificial Neural Networks

2 A Physicist's Crash Course on Artificial Neural Network

Published:
Category: { Artificial Neural Networks }
References: -
Summary: A very very brief introduction to neural network for physicists
Pages: 3

1 Artificial Neural Networks

Published:
Category: { Artificial Neural Networks }
References: - Hassoun MH, Assistant Professor of Computer Engineering Mohamad H Hassoun. Fundamentals of Artificial Neural Networks. MIT Press; 1995. Available: https://mitpress.mit.edu/books/fundamentals-artificial-neural-networks - Shenouda EAMA. A Quantitative Comparison of Different MLP Activation Functions in Classification. Advances in Neural Networks - ISNN 2006. Springer Berlin Heidelberg; 2006. pp. 849–857. doi:10.1007/11759966_125 - Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359–366. - Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, 2(4), 303–314. - Freitag, K. J. (2007). Neural networks and differential equations. - Tensorflow and deep learning - without a PhD by Martin Görner - Kolmogorov, A. N. (1957). On the Representation of Continuous Functions of Several Variables by Superposition of Continuous Functions of one Variable and Addition, Doklady Akademii. Nauk USSR, 114, 679-681. - Maxwell Stinchcombe, Halbert White (1989). Multilayer feedforward networks are universal approximators. Neural Networks, Vol 2, 5, 359-366. - Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks - Lippe P. Tutorial 3: Activation Functions — UvA DL Notebooks v1.1 documentation. In: UvA Deep Learning Tutorials [Internet]. [cited 23 Sep 2021]. Available: https://uvadlc-notebooks.readthedocs.io
Summary: Solving PDEs
Pages: 3