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Neural Network Design (Martin T. Hagan)
This book provides a clear and detailed coverage of fundamental neural network architectures and learning rules. It emphasizes a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems.

Neural Networks (Milan Hajek)
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 multidimensional scientific data, and relates neural networks to statistics.

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.

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.

A Brief Introduction to Neural Networks using Java
This book introduces the Java programmer to the world of Neural Networks and Artificial Intelligence using SNIPE.

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.

Recurrent Neural Networks for Temporal Data Processing
By presenting the latest research work the book demonstrates how realtime recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction.

Recurrent Neural Networks (Xiaolin Hu, et al)
This book investigates the following Recurrent Neural Networks models which solve some practical problems, together with their corresponding analysis on stability and convergence.

Neural Network Toolbox for MATLAB (Howard Demuth, et al)
It provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, timeseries forecasting, and dynamic system modeling and control.

Neural Networks Using C# Succinctly (James McCaffrey)
This book teaches you how to create your own neural network to solve classification problems, or problems where the outcomes can only be one of several values.

Introduction to Neural Networks for C#, 2nd Edition (Jeff Heaton)
This book introduces the C# programmer to the world of Neural Networks and Artificial Intelligence.

C++ Neural Networks and Fuzzy Logic (Valluru B. Rao, et al)
Provides a logical and easytofollow progression through C++ programming for two of the most popular technologies for artificial intelligence: neural and fuzzy programming.

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.

Machine Learning, Neural and Statistical Classification (D. Michie)
Statistical, machine learning and neural network approaches to classification are all covered in this volume.

Introduction to Neural Networks for Java, 2nd Edition (Jeff Heaton)
This book introduces the Java programmer to the world of Neural Networks and Artificial Intelligence.

Introduction to Neural Networks with Java 1st Edition (Jeff Heaton)
This book introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures such as the feedforward backpropagation, Hopfield, and Kohonen networks are discussed.

Neural Networks
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