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Java Concurrency and Parallelism

You're reading from   Java Concurrency and Parallelism Master advanced Java techniques for cloud-based applications through concurrency and parallelism

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781805129264
Length 496 pages
Edition 1st Edition
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Author (1):
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Jay Wang Jay Wang
Author Profile Icon Jay Wang
Jay Wang
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Table of Contents (20) Chapters Close

Preface 1. Part 1: Foundations of Java Concurrency and Parallelism in Cloud Computing FREE CHAPTER
2. Chapter 1: Concurrency, Parallelism, and the Cloud: Navigating the Cloud-Native Landscape 3. Chapter 2: Introduction to Java’s Concurrency Foundations: Threads, Processes, and Beyond 4. Chapter 3: Mastering Parallelism in Java 5. Chapter 4: Java Concurrency Utilities and Testing in the Cloud Era 6. Chapter 5: Mastering Concurrency Patterns in Cloud Computing 7. Part 2: Java's Concurrency in Specialized Domains
8. Chapter 6: Java and Big Data – a Collaborative Odyssey 9. Chapter 7: Concurrency in Java for Machine Learning 10. Chapter 8: Microservices in the Cloud and Java’s Concurrency 11. Chapter 9: Serverless Computing and Java’s Concurrent Capabilities 12. Part 3: Mastering Concurrency in the Cloud – The Final Frontier
13. Chapter 10: Synchronizing Java’s Concurrency with Cloud Auto-Scaling Dynamics 14. Chapter 11: Advanced Java Concurrency Practices in Cloud Computing 15. Chapter 12: The Horizon Ahead 16. Index 17. Other Books You May Enjoy Appendix A: Setting up a Cloud-Native Java Environment 1. Appendix B: Resources and Further Reading

Generative AI and Java – a new frontier

Generative AI encompasses a set of technologies that enable machines to understand and generate content with minimal human intervention. This can include generating text, images, music, and other forms of media. The field is primarily dominated by ML and deep learning models.

Generative AI includes these key areas:

  • Generative models: These are models that can generate new data instances that resemble the training data. Examples include generative adversarial networks (GANs), variational autoencoders (VAEs), and Transformer-based models such as Generative Pre-trained Transformer (GPT) and DALL-E.
  • Deep learning: Most generative AI models are based on deep learning techniques that use neural networks with many layers. These models are trained using a large amount of data to generate new content.
  • NLP: This is a pivotal area within AI that deals with the interaction between computers and humans through natural language. The...
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