• Title Introduction to Artificial Neural Networks
• Author(s) Jan Larsen, et al.
• Publisher: Technical University of Denmark
• Hardcover: N/A
• eBook: PDF
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A
• Share This:  

Book Description

This fundamental book on Artificial Neural Networks (ANN) has its emphasis on clear concepts, ease of understanding and simple examples. Written for undergraduate students, the book presents a large variety of standard neural networks with architecture, algorithms and applications.

• N/A

• Amazon

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

• Introduction to Artificial Neural Networks (Jan Larsen, et al.)
• The Mirror Site (1) - PDF
• The Mirror Site (2) - PDF

• Neural Network Learning: Theoretical Foundations

  This book describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions.

• Machine Learning with Neural Networks (Bernhard Mehlig)

  This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. It provides comprehensive coverage of neural networks, their evolution, their structure, their applications, etc.

• 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.

• Applied Artificial Neural Networks (Christian Dawson)

  This book focuses on 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.

• 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.

• 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.

• Recurrent Neural Networks and Soft Computing (M. ElHefnawi)

  Advanced information regarding the theory, concepts and applications of recurrent neural networks and the field of soft computing has been highlighted in this elaborative book. Additional topics in this vein are the application of AI techniques to electromagnetic interference problems, etc.

• Recurrent Neural Networks for Temporal Data Processing

  By presenting the latest research work the book demonstrates how real-time recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction.

• 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.

• C++ Neural Networks and Fuzzy Logic (Valluru B. Rao, et al)

  Provides a logical and easy-to-follow progression through C++ programming for two of the most popular technologies for artificial intelligence: neural and fuzzy programming. Covers theory as well as practical working examples with reusable code.