Neural Network Programming with Java - Second Edition by Alan M. F. Souza

Key FeaturesLearn to build amazing projects using neural networks including forecasting the weather and pattern recognitionExplore the Java multi-platform feature to run your personal neural networks everywhereThis step-by-step guide will help you solve real-world problems and links neural network theory to their applicationBook DescriptionWant 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.What You Will LearnDevelop an understanding of neural networks and how they can be fittedExplore the learning process of neural networksBuild neural network applications with Java using hands-on examplesDiscover the power of neural networks unsupervised learning process to extract the intrinsic knowledge hidden behind the dataApply the code generated in practical examples, including weather forecasting and pattern recognitionUnderstand how to make the best choice of learning parameters to ensure you have a more effective applicationSelect and split data sets into training, test, and validation, and explore validation strategiesAbout the AuthorFabio 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 is computer engineer from Instituto de Estudos Superiores da Amazonia (IESAM). He holds a post-graduate degree in project management software and a masters 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.Table of ContentsGetting Started with Neural NetworksGetting Neural Networks to LearnPerceptrons and Supervised LearningSelf-Organizing MapsForecasting WeatherClassifying Disease DiagnosisClustering Customer ProfilesText RecognitionOptimizing and Adapting Neural NetworksCurrent Trends in Neural NetworksReferences