Large Language Models From Foundations to Production AI Applications - Second Edition
-
- Englisch ausgewählt
38,99 €
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
23.08.2026
Verlag
Amazon Digital Services LLC - KdpSeitenzahl
532
Maße (L/B/H)
27,9/21,6/2,7 cm
Gewicht
1220 g
Sprache
Englisch
EAN
9798194493814
Large Language Models: From Foundations to Production AI Applications - Second Edition
Learn how Large Language Models work-and how to build, customize, and deploy real-world AI applications from development to production.
Large Language Models (LLMs) such as GPT, Claude, Gemini, Llama, Mistral, and DeepSeek are transforming software development, business automation, search, content generation, knowledge management, and enterprise AI. As organizations adopt Generative AI at scale, there is growing demand for professionals who understand not only how LLMs work, but also how to build, customize, deploy, secure, and operate AI-powered applications.
Updated for the Second Edition, this practical, hands-on guide provides a structured learning path from the foundations of Artificial Intelligence, Natural Language Processing, and Generative AI to Transformer architectures, modern LLMs, Retrieval-Augmented Generation (RAG), fine-tuning, and production AI systems. Rather than focusing only on theory, the book combines technical concepts with architecture diagrams, hands-on labs, code examples, and end-to-end projects that demonstrate how modern LLM applications are designed and built.
What You'll LearnFoundations of AI, Machine Learning, Deep Learning, NLP, and Generative AI
How LLMs work, including tokenization, embeddings, attention, and Transformer architecture
Encoder, decoder, BERT, GPT, and modern language-model architectures
Major LLM families, including GPT, Claude, Gemini, Llama, Mistral, and DeepSeek
Accessing LLMs through APIs and running local models with Ollama
Prompt engineering, structured outputs, function calling, tool usage, and reliable LLM workflows
Retrieval-Augmented Generation (RAG), embeddings, vector databases, semantic search, hybrid search, and re-ranking
Fine-tuning and efficient model adaptation using LoRA, QLoRA, PEFT, adapter layers, and quantization
Building AI chatbots, enterprise knowledge assistants, PDF applications, and content-generation systems
LLM evaluation, hallucination detection, security, guardrails, safety, and responsible AI practices
Production deployment with REST APIs, FastAPI, Docker, cloud platforms, and serverless architectures
Monitoring, logging, caching, load balancing, cost optimization, GPU inference, vLLM, scalability, and high availability
AI agents and agentic workflows
Apply what you learn by building complete end-to-end AI solutions:
AI Chatbot - Build a conversational AI application using modern LLM APIs, prompt engineering, backend integration, and deployment.
Enterprise Knowledge Assistant - Build a RAG-powered assistant using embeddings, vector search, document retrieval, and organizational knowledge.
Local AI Assistant - Build a private AI assistant using Ollama, local LLMs, and a custom knowledge base that can operate locally.
Source Code on GitHubCode examples, hands-on labs, and project source code are available through a dedicated GitHub repository, allowing you to follow the implementations, experiment with the examples, and build the solutions yourself.
Master the technologies behind modern Large Language Models and start building intelligent, production-ready AI applications.
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