Mastering Transformers for Practical NLP: A Hands on guide to BERT,GPT, T5,LLAMA,RAG,LORA and real world NLP applications using Hugging Face, Langchain and Open LLM frameworks - Tapa blanda

Sinopsis

Mastering Transformers for Practical NLP
A Hands-On Guide to BERT, GPT, T5, LLaMA, RAG, LoRA, and Real-World NLP Applications Using Hugging Face, LangChain, and Open LLM Frameworks
Build Powerful NLP Systems. Fine-Tune Open LLMs. Deploy Transformers That Work in the Real World.
Transformers revolutionized natural language processing — and this book shows you how to put them to work. Whether you're a machine learning engineer, data scientist, software developer, or AI startup builder, Mastering Transformers for Practical NLP is your complete guide to building, fine-tuning, and deploying transformer-powered applications.
Inside This Book:
• Train and fine-tune models like BERT, GPT, T5, LLaMA, and Falcon
• Use Hugging Face Transformers, PEFT, LangChain, and ChromaDB
• Implement end-to-end systems for sentiment analysis, summarization, QA, chatbots, and more
• Apply Retrieval-Augmented Generation (RAG) using FAISS and custom document stores
• Serve models using FastAPI, Gradio, and Streamlit
• Optimize workflows with LoRA, QLoRA, quantization, and distillation
• Build and deploy full-stack LLM agents, APIs, and UI prototypes
What Makes This Book Different:
• Developer-first design — step-by-step guides, diagrams, and working code
• Production-focused — every technique is grounded in real-world deployment
• Framework-agnostic thinking — learn Hugging Face, LangChain, open models, and more
• Hands-on mastery — every chapter ends with challenge drills and deployment blueprints
• Visual clarity — uses ASCII diagrams, flowcharts, checklists, and tables instead of images
Who This Book Is For:
• Engineers and developers breaking into NLP or LLM engineering
• Teams building internal NLP tools or customer-facing AI products
• AI researchers looking for practical implementations of transformers
• Indie hackers, startup builders, and solopreneurs using open-source LLMs
Includes:
• 20 project-based chapters from core concepts to production APIs
• Full coverage of tokenization, attention, sequence-to-sequence models, and agentic systems
• Appendices with cheat sheets, CLI commands, environment setup, and ready-to-use prompt templates
If you're ready to go beyond tutorials and truly master transformers for real-world NLP, this book is your blueprint.
Get ready to build. Fine-tune. Deploy. Scale.
Your journey into applied transformer engineering starts here.

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