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Deep Learning on the JVM. Build a Document Intelligence Platform with JVM Deep Learning
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Disponible dans votre compte client Decitre ou Furet du Nord dès validation de votre commande. Le format ePub est :
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- FormatePub
- ISBN8259603790
- EAN9798259603790
- Date de parution29/06/2026
- Protection num.pas de protection
- Taille850 Ko
- Infos supplémentairesepub
- ÉditeurChiify
Résumé
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.
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.