• Title Applied Artificial Neural Networks
• Author(s) Christian Dawson
• Publisher: Mdpi AG; 1. 2016 ed. edition (November 11, 2016)
• Hardcover/Paperback: 244 pages
• eBook: PDF
• Language: English
• ISBN-10: 3038422703
• ISBN-13: 978-3038422709
• Share This:  

Book Description

Since their re-popularisation in the mid-1980s, artificial neural networks have seen an explosion of research across a diverse spectrum of areas. While an immense amount of research has been undertaken in artificial neural networks themselves--in terms of training, topologies, types, etc. - a similar amount of work has examined their application to a whole host of real-world problems. Such problems are usually difficult to define and hard to solve using conventional techniques. Examples include computer vision, speech recognition, financial applications, medicine, meteorology, robotics, hydrology, etc.

This Special Issue focuses on the second of these two research themes, that of the application of neural networks to a diverse range of fields and problems. It collates contributions concerning neural network applications in areas such as engineering, hydrology and medicine.

• João Luís Garcia Rosa is an associate professor at the Department of Computer Science, University of São Paulo (USP) at Sao Carlos, Brazil, where he teaches disciplines such as Neural Networks, Brain-Computer Interfaces, and Artificial Intelligence.

• Amazon

• Neural Networks and Deep Learning
• Machine Learning
• Artificial Intelligence

• Applied Artificial Neural Networks (Christian Dawson)
• The Mirror Site (1) - PDF

• Deep Learning (Ian Goodfellow, et al)

  Written by three experts, this is the only comprehensive book on the subject. It offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.

• Neural Networks and Deep Learning (Michael Nielsen)

  Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.

• Neural Networks - A Systematic Introduction (Raul Rojas)

  In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. It is aimed at readers who seek an overview of the field or who wish to deepen their knowledge.

• An Introduction to Neural Networks (Kevin Gurney)

  With an easy to understand format using graphical illustrations and multidisciplinary scientific context, this book fills the gap in the market for neural networks for multi-dimensional scientific data, and relates neural networks to statistics.

• A Brief Introduction to Neural Networks (David Kriesel)

  Introduces the Java programmer to the world of Neural Networks and Artificial Intelligence using SNIPE. Examples include the traveling salesman problem, handwriting recognition, financial prediction, game strategy, mathematical functions, and Internet bots.

• Neural Networks with JavaScript Succinctly (James McCaffrey)

  This book leads you through the fundamental concepts of neural networks, including its architecture, its input-output, tanh and softmax activation, back-propagation, error and accuracy, normalization and encoding, and model interpretation.

• Artificial Neural Networks - Models and Applications

  This is a current book on Artificial Neural Networks and Applications, bringing recent advances in the area to the reader interested in this always-evolving machine learning technique. It contains chapters on basic concepts of artificial neural networks.

• Artificial Neural Networks - Architectures and Applications

  This book covers architectures, design, optimization, and analysis of artificial neural networks as well as applications of artificial neural networks in a wide range of areas including biomedical, industrial, physics, and financial applications.

• Artificial Neural Networks - Methodological Advances and Apps

  The book begins with fundamentals of artificial neural networks, which cover an introduction, design, and optimization. Advanced architectures for biomedical applications, which offer improved performance and desirable properties, follow.

• Neural Networks (Rolf Pfeifer, et al)

  Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic neural network concepts. It is a systematic introduction to neural networks, biological foundation.

• Memristor and Memristive Neural Networks (Alex James)

  This book covers a range of models, circuits and systems built with memristor devices and networks in applications to neural networks. It is divided into three parts: (1) Devices, (2) Models and (3) Applications.