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Mastering NLP with Hugging Face

Leveraging diffusion models, transformers, and reinforcement learning for generative and analytical systems (English Edition)

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Auteur(s): H. Leocadio, Paulo

Editeur: BPB Publications

Année de Publication: 2026

pages: 336

ISBN: 978-93-6589-318-2

Description The most influential libraries in modern AI, Hugging Face Diffusers has become one of the key powering breakthroughs in text-to-image generation, reinforcement learning, and large-scale inference pipelines. This book bridges theory and real-world implementation, enabling readers to trans
Description
The most influential libraries in modern AI, Hugging Face Diffusers has become one of the key powering breakthroughs in text-to-image generation, reinforcement learning, and large-scale inference pipelines. This book bridges theory and real-world implementation, enabling readers to translate complex AI concepts into scalable, production-ready solutions.

This book offers a comprehensive, practical, and academic exploration of the diffusers ecosystem, beginning with foundational concepts of the Hugging Face library, progressing to multimodal diffusion, schedulers, RL algorithms (DQN, A3C, AlphaZero), and real-world deployment patterns across cloud platforms. Each chapter offers hands-on examples, design insights, and conceptual explanations that guide you from fundamentals to production-grade workflows.

By the end of this book, readers will have the skills to build, train, evaluate, and deploy state-of-the-art diffusion models and reinforcement learning agents, while applying ethical and responsible AI practices across their work.

What you will learn
? Build diffusion pipelines for NLP and vision tasks.
? Train DQN, A3C, and AlphaZero RL agents.
? Use schedulers for stable and efficient inference.
? Deploy models across AWS, GCP, and Azure.
? Apply ethical and responsible AI patterns.
? Optimize performance with MLOps workflows.

Who this book is for
This book is written for ML engineers, cloud architects, cybersecurity analysts, generative AI developers, and researchers seeking a rigorous and practical guide to diffusion models and reinforcement learning. It is ideal for professionals designing scalable, ethical, and production-ready diffusion models and AI systems.

Table of Contents
1. Introduction to Hugging Face Diffusers Library
2. Utilizing Hugging Face Diffusers for Text Classification
3. Advanced Generative Tasks with Hugging Face Diffusers
4. Sequence Labeling with Hugging Face Diffusers
5. Transfer Learning for NLP Tasks
6. Pipelines in Hugging Face Diffusers
7. Schedulers in Hugging Face Diffusers
8. Advanced Inference Techniques
9. Build Your Own AlphaZero AI
10. Deep Q-Network and Atari Games
11. Asynchronous Actor-Critic with Gym-Retro
12. Road Ahead
References

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