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.
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