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Neural Network Programming with Java - Second Edition, by Fabio M. Soares, Alan M. F. Souza
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Create and unleash the power of neural networks by implementing professional Java code
About This Book- Learn to build amazing projects using neural networks including forecasting the weather and pattern recognition
- Explore the Java multi-platform feature to run your personal neural networks everywhere
- This step-by-step guide will help you solve real-world problems and links neural network theory to their application
This book is for Java developers who want to know how to develop smarter applications using the power of neural networks. Those who deal with a lot of complex data and want to use it efficiently in their day-to-day apps will find this book quite useful. Some basic experience with statistical computations is expected.
What You Will Learn- Develop an understanding of neural networks and how they can be fitted
- Explore the learning process of neural networks
- Build neural network applications with Java using hands-on examples
- 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
Want to discover the current state-of-art in the field of neural networks that will let you understand and design new strategies to apply to more complex problems? This book takes you on a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java, giving you everything you need to stand out.
You will first learn the basics of neural networks and their process of learning. We then focus on what Perceptrons are and their features. Next, you will implement self-organizing maps using practical examples. Further on, you will learn about some of the applications that are presented in this book such as weather forecasting, disease diagnosis, customer profiling, generalization, extreme machine learning, and characters recognition (OCR). Finally, you will learn methods to optimize and adapt neural networks in real time.
All the examples generated in the book are provided in the form of illustrative source code, which merges object-oriented programming (OOP) concepts and neural network features to enhance your learning experience.
Style and approachThis book takes you on a steady learning curve, teaching you the important concepts while being rich in examples. You'll be able to relate to the examples in the book while implementing neural networks in your day-to-day applications.
- Sales Rank: #1667024 in Books
- Published on: 2018-01-04
- Released on: 2017-03-14
- Original language: English
- Dimensions: 9.25" h x .61" w x 7.50" l,
- Binding: Paperback
- 269 pages
About the Author
Fabio M. Soares
Fabio M. Soares is currently a PhD candidate at the Federal University of Para (Universidade Federal do Para - UFPA), in northern Brazil. He is very passionate about technology in almost all fields, and designs neural network solutions since 2004 and has applied this technique in several fields like telecommunications, industrial process control and modeling, hydroelectric power generation, financial applications, retail customer analysis and so on. His research topics cover supervised learning for data-driven modeling. As of 2017, he is currently carrying on research projects with chemical process modeling and control in the aluminum smelting and ferronickel processing industries, and has worked as a lecturer teaching subjects involving computer programming and artificial intelligence paradigms. As an active researcher, he has also a number of articles published in English language in many conferences and journals, including four book chapters.
Alan M. F. Souza
Alan M. F. Souza is computer engineer from Instituto de Estudos Superiores da Amazonia (IESAM). He holds a post-graduate degree in project management software and a master's degree in industrial processes (applied computing) from Universidade Federal do Para (UFPA). He has been working with neural networks since 2009 and has worked with Brazilian IT companies developing in Java, PHP, SQL, and other programming languages since 2006. He is passionate about programming and computational intelligence. Currently, he is a professor at Universidade da Amazonia (UNAMA) and a PhD candidate at UFPA.
Most helpful customer reviews
0 of 0 people found the following review helpful.
HUGELY frustrating!!
By just another geek
Purchased the paper version...and omigosh, this thing is mind-numbingly frustrating!!
Most significantly, this is NOT a learn-by-doing book. Snippets of code are presented in the book, so you're not entering the stuff yourself -- just compiling what they've done. Speaking of which...be SURE to get the code from github, NOT via the link emailed from the publisher's web site, because THAT version of the code isn't even close to reflecting what's in the text. (Did I mention frustrating?)
Oh, you want an explanation of the code? No problem! The book promises (page 19) that "In appendix C, we present the reader the full documentation of the classes along with their UML class and package diagrams that will surely help as a reference for the book." EXCEPT that (1) appendix C does not appear in the book, and (2) the Preface describes the chapters and appendices, noting that the Appendices must be downloaded as a PDF...except that (a) appendix C isn't even mentioned here, and (b) not surprisingly, it is NOT part of the downloaded PDF. (Be careful with the really long name that's given -- no shortened URL here! -- as the 'W' must be lowercase.)
Taken together, not only was this book purchase a waste of both time and money, but there's not a chance I'll ever purchase another book from this publisher, who seems heck-bent on saving paper rather than, you know, teaching.
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