Artificial Neural Networks
by B. Mehlig
Publisher: arXiv.org 2019
Number of pages: 206
These are lecture notes for my course on Artificial Neural Networks that I have given at Chalmers and Gothenburg University. This course describes the use of neural networks in machine learning: deep learning, recurrent networks, and other supervised and unsupervised machine-learning algorithms.
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by Todd Troyer - University of Texas at San Antonio
These notes have three main objectives: to present the major concepts of computational neuroscience, to present the basic mathematics that underlies these concepts, and to give the reader some idea of common approaches taken by neuroscientists.
by David Kriesel - dkriesel.com
Text and illustrations should be memorable and easy to understand to offer as many people as possible access to the field of neural networks. The chapters are individually accessible to readers with little previous knowledge.
by Jeff Heaton - Heaton Research
The book is an introduction to Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques are also introduced.
by Raul Rojas - Springer
A general theory of artificial neural nets. The book starts with the simple nets, and shows how the models change when more general computing elements and net topologies are introduced. Suitable as a basis for university courses in neurocomputing.