Beyond Artificial : A Practical Guide in Synthetic Intelligence by Ajit Singh
English | October 18, 2025 | ISBN: N/A | ASIN: B0FGYDRZQY | 369 pages | EPUB | 1.21 Mb
"Beyond Artificial: A Practical Guide in Synthetic Intelligence" is a comprehensive, hands-on textbook designed for the next generation of innovators, engineers, and thinkers. It reimagines the study of AI, shifting the focus from merely mimicking human intelligence to the engineering of novel, purpose-driven intelligent systems. This book serves as a complete course, guiding learners from the foundational principles of AI to the deployment of sophisticated, real-world applications.

To Whom is this Book For?
This book is meticulously crafted for a diverse audience, catering to learners at various stages of their technical journey:
1. Undergraduate and Postgraduate Students: B.Tech/B.E. and M.Tech/M.E. students in Computer Science, Information Technology, and related disciplines will find this an indispensable primary textbook for their AI and Machine Learning courses.
2. Software Developers and IT Professionals: Practitioners looking to upskill, transition into AI/ML roles, or integrate intelligent features into their applications will benefit from the book's practical, code-first approach.
3. AI/ML Enthusiasts and Self-Learners: Individuals with a passion for technology who want a structured, accessible, and self-contained guide to mastering Synthetic Intelligence.
4. Academicians and Researchers: Educators can use this book as a primary teaching resource, while researchers will find its comprehensive coverage of modern techniques a valuable reference.
Key Features of the Book:
1. Beginner to Advanced Progression: The 10-chapter structure provides a logical and smooth learning curve, making it suitable for both novices and those looking to deepen their expertise.
2. Hands-On & Practical: Packed with code examples, practical labs, and implementation details using popular Python libraries like TensorFlow, PyTorch, and Scikit-learn.
3. Real-World Case Studies: Each major concept is reinforced with a case study (e.g., spam detection, medical image analysis, sentiment analysis) that demonstrates its real-world impact.
4. End-to-End Capstone Project: The final chapter is a complete, do-it-yourself project that guides the reader through building, training, and deploying a live intelligent system from scratch.
5. Simplified Concepts: Complex algorithms and mathematical concepts are broken down into simple, intuitive explanations with illustrative diagrams and practical analogies.
6. Focus on Modern Architectures: Provides up-to-date coverage of cutting-edge topics, including MLOps, Transformer models, and Reinforcement Learning frameworks.
7. Ethical Considerations: An entire chapter is dedicated to the crucial topics of AI ethics, fairness, accountability, and the societal impact of intelligent systems.
This book is my humble attempt to create a resource that I wished I had as a student-one that is both academically rigorous and immensely practical. It is for the B.Tech student taking their first AI course, the M.Tech scholar seeking to master advanced concepts, the software professional aiming to pivot their career, and the curious enthusiast who wants to do more than just read about AI-they want to build it.

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