Java deep learning build neural networks Java JVM machine learning If you're a Java developer ready to dive into AI without leaving your familiar ecosystem, this book is your hands-on guide. From tensors and layers to training loops and deployment, you'll learn to build and ship production-ready deep learning models entirely on the JVM. No Python required. Start with the fundamentals: tensors, automatic differentiation, and the core building blocks of neural networks. Progress through hands-on chapters that teach you to construct layers, implement forward and backward passes, and write custom training loops. You'll master key concepts like activation functions, loss functions, optimizers, and regularization—all in Java. The book then moves to advanced topics: convolutional networks for image data, recurrent networks for sequences, and attention mechanisms. Each chapter includes complete code examples you can run immediately. Deployment is a first-class concern. You'll learn to export models, integrate with Spring Boot, serve predictions via REST APIs, and optimize for performance using JVM profiling tools. Real-world case studies show you how to apply deep learning to recommendation systems, anomaly detection, and natural language processing—all within your existing Java stack. By the end, you'll be able to design, train, evaluate, and deploy neural networks that solve practical business problems. Who this book is for: Java developers, data engineers, and software architects who want to add deep learning to their toolkit without learning a new language. Prior machine learning experience is helpful but not required. The book assumes you're comfortable with Java 11+ and basic OOP concepts. Competitor books like Machine Learning System Design Interview and Building LLMs for Production cover system design or LLM-specific topics, but none focus on hands-on JVM deep learning from scratch. This book fills that gap—giving you the code, the theory, and the deployment know-how to build neural networks in Java.
