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 | Neural Network Programming with Java - Second Edition | Fabio M. Soares, Alan M. F. Souza | ebookpoint |
| ebookpoint.pl | od 159.00PLN | ||
 Neural Network Programming with Java - Second Edition za 159.00 zł Cena: 159.00 zł Create and unleash the power of neural networks by implementing professional Java codeAbout This BookLearn 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 applicationWho This Book Is ForThis 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 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 network's 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 strategiesIn DetailWant 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. | |||
