Developing LangGraph Apps From Scratch
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Narrado por:
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Virtual Voice
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De:
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Ajit Singh
Este título utiliza narración de voz virtual
Voz Virtual es una narración generada por computadora para audiolibros..
Philosophy
The core philosophy of this book is "learning by building." I believe that proficiency in a technical framework is best achieved through direct, hands-on application. Consequently, theoretical exposition is kept concise and purposeful, serving as a direct prelude to practical implementation. The book eschews abstract discussions in favor of tangible, code-first examples that demonstrate concepts in action. It is designed to be a companion at your keyboard, guiding you as you write code, debug, and bring your own intelligent applications to life.
Key Features
1. Step-by-Step Guidance: From setting up the development environment to deploying a full-scale application, the book provides clear, sequential instructions.
2. Hands-On Practicals: Rich with code examples, exercises, and mini-projects that reinforce learning and build practical skills.
3. Real-World Case Studies: Demonstrates the application of LangGraph in building practical tools like research assistants, customer support agents, and code generation systems.
4. Focus on Modern Architectures: Covers essential modern concepts like state management, multi-agent collaboration, and human-in-the-loop workflows.
5. Complete Capstone Project: A dedicated final chapter guides the reader through building a complete, live Do-It-Yourself (DIY) project from scratch, integrating all the concepts learned throughout the book.
6. Beginner to Advanced: The content is structured to cater to beginners while also providing depth and advanced topics for experienced developers looking to master agentic workflows.
To Whom This Book Is For
1. B.Tech/M.Tech Students: Computer Science and Information Technology students seeking a practical, syllabus-compliant resource for courses on Artificial Intelligence, Machine Learning, or a specialized elective on LLM Application Development.
2. Software Developers: Professionals who want to transition into AI development or integrate intelligent, agentic capabilities into their existing applications.
3. AI/ML Practitioners: Data scientists and machine learning engineers who are familiar with LLMs but want to master the art of building robust, stateful, and multi-step agentic systems.
4. Researchers and Academics: Individuals exploring novel architectures for agent-based systems and looking for a practical guide to implementing their ideas using a powerful, flexible framework.
Disclaimer: Earnest request from the Author.
Kindly go through the table of contents and refer kindle edition for a glance on the related contents.
Thank you for your kind consideration!
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