FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World


 Title: Neural Network Programming with Java
 Author(s) Alan M.F. Souza, Fabio M. Soares
 Publisher: Packt Publishing  ebooks Account (January 15, 2016)
 Paperback: 244 pages
 eBook: PDF
 Languages: English
 ISBN10: 178588090X
 ISBN13: 9781785880902
 Share This:
Book Description
This book gives you a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java.
This book is for Java developers with basic Java programming knowledge. No previous knowledge of neural networks is required as this book covers the concepts from scratch.
Vast quantities of data are produced every second. In this context, neural networks become a powerful technique to extract useful knowledge from large amounts of raw, seemingly unrelated data. One of the most preferred languages for neural network programming is Java as it is easier to write code using it, and most of the most popular neural network packages around already exist for Java. This makes it a versatile programming language for neural networks.
This book adopts a stepbystep approach to neural network development and provides many handson examples using Java programming. Each neural network concept is explored through realworld problems and is delivered in an easytocomprehend manner.
 Get to grips with the basics of neural networks and what they are used for
 Develop neural networks using handson examples
 Explore and code the most widelyused learning algorithms to make your neural network learn from most types of data
 Discover the power of neural network's unsupervised learning process to extract the intrinsic knowledge hidden behind the data
 Apply the code generated in practical examples, including weather forecasting and pattern recognition
 Understand how to make the best choice of learning parameters to ensure you have a more effective application
 Select and split data sets into training, test, and validation, and explore validation strategies
 Discover how to improve and optimize your neural network
 Alan M.F. Souza is computer engineer from Instituto de Estudos Superiores da Amazonia (IESAM).
 Fabio M. Soares holds a master's degree in applied computing from UFPA and is currently a PhD candidate at the same university. He has been designing neural network solutions since 2004 and has developed applications with this technique in several fields, ranging from telecommunications to chemistry process modeling, and his research topics cover supervised learning for datadriven modeling.
 Neural Networks
 Advanced Java
 Machine Learning
 Artificial Intelligence, Machine Learning, and Logic Programming
 Data Analysis and Data Mining, Big Data

A Brief Introduction to Neural Networks using Java
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.

Practical Artificial Intelligence Programming in Java (Mark Watson)
This book uses both best of breed open source software and the author's own libraries to introduce the reader to Artificial Intelligence (AI) technologies like genetic algorithms, neural networks, expert systems, machine learning, etc.

Introduction to Neural Networks with Java (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.

Programming Neural Networks with Encog3 in Java (Jeff Heaton)
This book focuses on using the neural network capabilities of Encog with the Java programming language. It begins with an introduction to the kinds of tasks neural networks are suited towards.

Neural Networks with JavaScript Succinctly (James McCaffrey)
This book leads you through the fundamental concepts of neural networks, including its architecture, its inputoutput, tanh and softmax activation, backpropagation, error and accuracy, normalization and encoding, and model interpretation.

Deep Learning with JavaScript: Neural Networks in TensorFlow.js
This book shows developers how they can bring Deep Learning technology to the web. Written by the main authors of the TensorFlow library, it provides fascinating use cases and indepth instruction for deep learning apps in JavaScript in your browser or on Node.

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 with Neural Networks (Bernhard Mehlig)
This modern and selfcontained 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.

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.

Advanced Applications for Artificial Neural Networks (Adel E.)
In this book, highly qualified multidisciplinary scientists grasp their recent researches motivated by the importance of Artificial Neural Networks (ANN). It addresses advanced applications and innovative case studies for different fields using ANN.

Deep Neural Networks and Data for Automated Driving
This open access book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving and artificial intelligence.

Deep Learning for Coders with Fastai and PyTorch
This book show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.

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 multidimensional scientific data, and relates neural networks to statistics.

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.
:






















