Neural Network Programming with Java – Second Edition

Neural Network Programming with Java – Second Edition

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 DescriptionThis book will help you discover the current state-of-art in the field of neural networks, enabling you to 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.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 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 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 strategies

Author: Alan M.F. Souza

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