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 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 Robert Fuller - Abo Akademi University
This text covers inference mechanisms in fuzzy expert systems, learning rules of feedforward multi-layer supervised neural networks, Kohonen's unsupervised learning algorithm for classification of input patterns, and fuzzy neural hybrid systems.
by William Bialek - arXiv
We all are fascinated by the phenomena of intelligent behavior, as generated by our own brains. As physicists we want to understand if there are some general principles that govern the dynamics of the neural circuits that underlie these phenomena.
by Xiaolin Hu, P. Balasubramaniam - InTech
The concept of neural network originated from neuroscience, and one of its aims is to help us understand the principle of the central nerve system through mathematical modeling. The first part of the book is dedicated to this aim.