دانلود کتاب پایتون سطح متوسط ​​و مدل‌های زبان‌های بزرگ

The evolution of artificial intelligence (AI) has ushered in a new era of possibilities, transforming the way we interact with technology, automate tasks, and solve complex problems. At the heart of this revolution are large language models (LLMs), which power applications ranging from conversational agents to content generation, data retrieval, and beyond. This book serves as an advanced comprehensive guide to understanding, developing, and deploying LLM-powered applications, with an advanced focus on Python and LangChain. It is designed for AI enthusiasts, data scientists, machine learning engineers, developers, and researchers who are looking to deepen their understanding of LLMs and their real-world applications. The book bridges the gap between theory and practice, providing a road map for building advanced intelligent systems that leverage the power of language models. Throughout the chapters, I emphasize hands-on learning, providing code examples, best practices, and troubleshooting strategies to help you build efficient and effective AI-driven applications. By the end of your journey, you will have a strong foundation in LLMs and the ability to apply them to a wide range of real-world challenges. The book is divided into several key chapters, each focusing on a critical aspect of working with LLMs and LangChain. • Chapter 1—LangChain and Python: Basics: This chapter introduces the fundamentals of LangChain, a powerful framework for integrating LLMs into applications. It covers the core advanced concepts, including chains, memory, tools, and agents, along with how to structure prompts effectively for different tasks. • Chapter 2—LangChain and Python: Advanced Components: Building on the basics, this chapter explores LangChain’s advanced features, such as memory management, multiagent systems, and external data integrations. Readers will learn to create applications with contextual awareness and adaptability. • Chapter 3—Building Advanced Applications Powered by LLMs with LangChain and Python: This chapter delves into the development of practical applications using LangChain and Python. It includes real-world examples like YouTube video summarizers and document retrieval tools, demonstrating how to implement advanced workflows and optimize model performance. • Chapter 4—Deploying LLM-Powered Applications: Once an LLM application is built, deploying it effectively is crucial. This chapter covers cloud deployment strategies, model-serving solutions, optimization techniques, and best practices for ensuring scalability, security, and performance in production environments. • Chapter 5—Building and Fine-Tuning LLMs: For those looking to take customization further, this chapter explains the principles of training and fine-tuning LLMs. It discusses transformer architectures, pretraining paradigms, fine-tuning strategies, and ethical considerations in deploying AI responsibly.