The 7 Habits of Highly Effective People Stephen R. Covey 

The Official Guide for GMAT Review Graduate Management Admission Council 

The China Study: The Most Comprehensive Study of Nutrition Ever Conducted And the Startling Implications for Diet, Weight Loss, And Longterm Health Howard Lyman, John Robbins, T. Colin Campbell, Thomas M. Campbell Ii 

How to Read Literature Like a Professor: A Lively and Entertaining Guide to Reading Between the Lines Thomas C. Foster 

Too Big to Fail: The Inside Story of How Wall Street and Washington Fought to Save the Financial Systemand Themselves Andrew Ross Sorkin 

PMP Exam Prep Rita Mulcahy 

Adobe Photoshop CS5 Classroom in a Book Adobe Creative Team 
A Game of Thrones  A Song of Ice and Fire George R. R. Martin 

The Help Kathryn Stockett 

A Clash of Kings  A Song of Ice and Fire, Book II George R. R. Martin 

The Hunger Games Suzanne Collins 

Influencer: The Power to Change Anything Kerry Patterson 

Catching Fire  Hunger Games, Book 2 Suzanne Collins 

A Storm of Swords  A Song of Ice and Fire, Book III George R. R. Martin, Roy Dotrice 

Recurrent neural networks for prediction
5
2

New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate 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. Within this text neural networks are considered as massively interconnected nonlinear adaptive filters.
Ø Analyses the relationships between RNNs and various nonlinear models and filters, and introduces spatiotemporal architectures together with the concepts of modularity and nesting
Ø Examines stability and relaxation within RNNs
Ø Presents online learning algorithms for nonlinear adaptive filters and introduces new paradigms which exploit the concepts of a priori and a posteriori errors, datareusing adaptation, and normalisation
Ø Studies convergence and stability of online learning algorithms based upon optimisation techniques such as contraction mapping and fixed point iteration
Ø Describes strategies for the exploitation of inherent relationships between parameters in RNNs
Ø Discusses practical issues such as predictability and nonlinearity detecting and includes several practical applications in areas such as air pollutant modelling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing
Recurrent Neural Networks for Prediction offers a new insight into the learning algorithms, architectures and stability of recurrent neural networks and, consequently, will have instant appeal. It provides an extensive background for researchers, academics and postgraduates enabling them to apply such networks in new applications.
From the Back Cover
New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate 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. Within this text neural networks are considered as massively interconnected nonlinear adaptive filters. Analyses the relationships between RNNs and various nonlinear models and filters, and introduces spatiotemporal architectures together with the concepts of modularity and nesting
Examines stability and relaxation within RNNs
Presents online learning algorithms for nonlinear adaptive filters and introduces new paradigms which exploit the concepts of a priori and a posteriori errors, datareusing adaptation, and normalisation
Studies convergence and stability of online learning algorithms based upon optimisation techniques such as contraction mapping and fixed point iteration
Describes strategies for the exploitation of inherent relationships between parameters in RNNs
Discusses practical issues such as predictability and nonlinearity detecting and includes several practical applications in areas such as air pollutant modelling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing Recurrent Neural Networks for Prediction offers a new insight into the learning algorithms, architectures and stability of recurrent neural networks and, consequently, will have instant appeal. It provides an extensive background for researchers, academics and postgraduates enabling them to apply such networks in new applications.
I should buy it in 2001.
I give this book 5 stars. It is a must have book, very well written. It has good balance between rigorous theory and authors reasonong regarding the subject.
I'm not a beginner in this field, and still I found a lot of interesting ideas, that can help not only to improve quality of the net, but also make you see "bigger picture".
Unexpected insights that make you go: "Aha!"
"Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability," approaches the field of recurrent neural networks from both a practical and a theoretical perspective. Starting from the fundamentals, where unexpected insights are offered even at the level of the dynamical richness of simple neurons, the authors describe many existing algorithms and gradually introduce novel ones. The latter are convicingly shown to yield better prediction performances than traditional approaches, when applied to realworld data. They also dedicate a considerable amount of time on the (practical) issue of nonlinearity analysis of time series, which is or should be, indeed, the cradle of all proper modelling and/or filtering solutions: nonlinearity should be assessed prior to choosing the appropriate model and/or filters, since linear ones are to be preferred if sufficient for the problem. I would recommend this book to any researcher who is active in the field of recurrent neural networks and time series analysis, but also to researchers who are new in the field, since the book offers an extensive overview of the current stateoftheart approaches.
Introduction To Pattern Recognition: Statistical, Structural, Neural and Fuzzy Logic Approaches Abraham Kandel, Menahem Friedman 
Decoherence, entanglement and information protection in complex quantum systems A. Sarfati, G. Kurizki, S. Pellegrin, Vladimir M. Akulin 
Signal Processing Techniques for Knowledge Extr. and Infor. Fusion Anthony Kuh, Danilo Mandic, Dragan Obradovic, Martin Golz, Toshihisa Tanaka 

Artificial Neural Networks  ICANN 2007, 17 conf Danilo Mandic, Joaquim Marques De Sá, Luis A. Alexandre, Wlodzislaw Duch 


