دانلود کتاب یادگیری Mistral – ارتقاء سیستم‌های میسترال از طریق جاسازی‌ها، عامل‌ها، RAG، AWS Bedrock و Vertex AI

This book is a practical guide to working with Mistral models and Retrieval-Augmented Generation (RAG), designed for developers, data scientists, and technology professionals who want to bridge the gap between theory and application. It introduces the core concepts of large language models and embeddings, then progresses through hands-on workshops that cover building chat systems, tuning models, designing RAG pipelines, coding assistants, security applications, and deploying at scale with AWS Bedrock and Google Vertex AI. The book is intended for builders, tinkerers, and curious minds who learn best by doing. Each chapter combines foundational theory with modular workshops, allowing you to dive into the topics that excite you most while ensuring earlier sections prepare you with the necessary setup and mindset. The approach emphasizes practical skills with minimal costs—using Python, Google Colab, VS Code, and cloud services that can run on free tiers or modest budgets. While roughly two-thirds of the book is hands-on, the early theory chapters are not to be skipped. They establish the building blocks of LLMs, embeddings, and RAG, which make the later exercises more meaningful and effective. Each workshop is self-contained, but common setup steps such as API keys and environment configuration are introduced once and assumed in later chapters. For those ready to go further, extracurricular challenges are included throughout, encouraging experimentation beyond guided exercises and inspiring readers to extend their projects into new directions. In short, this book is both a workshop companion and a reference guide. It equips you with the knowledge and skills to build intelligent systems, while leaving room for creativity, exploration, and innovation. This book is designed to be equally interesting to data science professionals, software engineers and AI/LLM enthusiasts who want to build practical skills with Mistral models and RAG. Whether you are working in software engineering, data science, business intelligence, or industry applications, the book provides both the concepts and hands-on projects needed to apply AI effectively in real-world scenarios across domains. Working knowledge of Python and the basics of NumPy and pandas is helpful; however, the material remains accessible, with comprehensive step-by-step guides to bridge any gaps. Familiarity with data structures (lists, dictionaries, arrays) and core mathematical concepts in linear algebra, probability, and calculus will be helpful, though not strictly required. The book is designed to bridge theory with application, making advanced ideas accessible to anyone ready to experiment and build.