by Ivan F Wilde
Publisher: King's College London 2009
Number of pages: 126
These notes are based on lectures given some years ago in the Mathematics Department at King's College London (as part of the MSc programme in Information Processing and Neural Networks). An attempt has been made to present a reasonably logical (mathematical) account of some of the basic ideas of the "artificial intelligence" aspects of the subject.
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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 Mark Watson - Lulu.com
The book uses the author's libraries and the best of open source software to introduce AI (Artificial Intelligence) technologies like neural networks, genetic algorithms, expert systems, machine learning, and NLP (natural language processing).
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 B. Mehlig - arXiv.org
These are lecture notes for my course on Artificial Neural Networks. This course describes the use of neural networks in machine learning: deep learning, recurrent networks, and other supervised and unsupervised machine-learning algorithms.