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Beyond GPT: Neuro-Symbolic AI

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Beyond GPT: Neuro-Symbolic AI

By: Ajit Singh
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"Beyond GPT: Neuro-Symbolic AI" provides a comprehensive and accessible pathway into one of the most promising and critical areas of modern Artificial Intelligence. As the limitations of purely data-driven models like GPT become more apparent, the need for systems that combine learning with reasoning has never been more urgent. This book serves as both a theoretical guide and a practical handbook for understanding, designing, and implementing Neuro-Symbolic AI systems.


To Whom This Book Is For:


1. B.Tech/M.Tech Computer Science Students: The book is designed as a primary textbook for courses on Advanced AI, Knowledge Representation, Machine Learning, or as a specialized elective, aligning perfectly with modern university syllabi worldwide.
2. AI & Machine Learning Practitioners: Developers and engineers working with deep learning models will find invaluable techniques for overcoming common challenges like explainability, catastrophic forgetting, and out-of-distribution generalization.
3. Academic Researchers: The book provides a structured overview of the current landscape of Neuro-Symbolic AI, serving as a strong foundation for further research in the field.
4. Aspiring AI Enthusiasts: Individuals with a basic programming and machine learning background will find the book's progressive, step-by-step approach an ideal entry point into this cutting-edge domain.


Key Features:


1. Beginner to Advanced Progression: The book logically progresses from the fundamental building blocks of both neural and symbolic AI to advanced hybrid architectures and applications.
2. Hands-On Practical Approach: Rich with Python code examples, practical tutorials, and hands-on exercises to reinforce learning and bridge the gap between theory and practice.
3. Complete Capstone Project: A dedicated final chapter guides the reader step-by-step through building a fully functional Neuro-Symbolic Question Answering system, complete with code and explanations.
4. Focus on Explainability (XAI): A dedicated chapter explores how Neuro-Symbolic methods can be used to build transparent and trustworthy AI systems, a critical need in today's world.
5. Simple and Lucid Language: Complex mathematical and logical concepts are explained in the simplest possible terms, using intuitive analogies and practical, real-life examples.
6. Real-World Case Studies: Explores the application of Neuro-Symbolic AI in diverse domains such as healthcare, robotics, finance, and natural language understanding.


Every chapter is imbued with a practical-first philosophy. Abstract theories are immediately grounded with simple, relatable examples and hands-on code snippets. We culminate this learning journey with a comprehensive, do-it-yourself capstone project where you will build a complete Neuro-Symbolic system from scratch.

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!
Computer Science Data Science Machine Learning Artificial Intelligence Programming
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