Artificial Intelligence and Neural Networks

NEP 20 Compliant

"Artificial Intelligence and Neural Networks" is meticulously crafted to serve as a comprehensive and contemporary guide for undergraduate (B.Tech) and postgraduate (M.Tech) students navigating the intricate yet fascinating landscape of AI. In an era where AI is reshaping industries and everyday life, this book aims to equip students with a robust theoretical foundation, practical insights, and an ethical understanding of AI technologies, aligning seamlessly with the forward-thinking National Education Policy (NEP) 2020, the rigorous standards of the All India Council for Technical Education (AICTE), and the core AI curricula of leading universities worldwide.

Key Features of This Book:

1. NEP 2020 and AICTE Compliant: The content, structure, and pedagogical approach are in strict adherence to the latest guidelines. Emphasis is placed on multidisciplinary learning, critical thinking, problem-solving, ethical considerations, and practical, skill-based education, including an Indian context where relevant.
2. Globally Relevant Curriculum: While tailored for the Indian educational system, the topics covered are universally recognized and form the core of AI and NN syllabi in top international universities, ensuring students are globally competitive.
3. Comprehensive 10-Chapter Structure: The book is organized into ten logically sequenced chapters, covering the entire spectrum from AI fundamentals to advanced deep learning models and their societal implications. This focused structure ensures thorough coverage without overwhelming the learner.
4. Updated and Latest Content: We have incorporated the most recent advancements in AI and Neural Networks, including detailed discussions on Transformers (BERT, GPT), GANs, advanced CNN and RNN variants, federated learning, and graph neural networks.
5. Balanced Theoretical and Practical Approach: Each concept is explained with theoretical depth, followed by illustrative examples, pseudocode, and discussions on practical implementation. This encourages students to not just learn what AI is, but how to build and apply it.
6. Emphasis on Ethical AI: Integrated throughout the text, and particularly in dedicated sections, are discussions on AI ethics, fairness, bias, transparency, accountability, and the societal impact of AI. This aligns with NEP 2020's call for responsible innovation.
7. Clear and Accessible Language: Complex mathematical and algorithmic concepts are broken down into simpler terms, making them accessible to students from diverse backgrounds, without sacrificing academic rigor.
8. Rich Learning Aids: The book will be (or is intended to be) supplemented with numerous diagrams, flowcharts, comparative tables, and real-world examples to enhance understanding and retention. (While not explicitly requested to write these, this feature is crucial for such a book).
9. Focus on Applications and Case Studies: Chapter 9 is dedicated to diverse AI applications across various sectors like healthcare, finance, NLP, and computer vision, supported by illustrative case studies that demonstrate real-world impact and encourage project-based learning.
10. Future-Oriented Perspective: The concluding chapter explores emerging trends, future research directions, and the evolving role of AI, preparing students for lifelong learning in this dynamic field.

I hope that "Artificial Intelligence and Neural Networks" will serve as a catalyst for learning and discovery. I envision it empowering a new generation of engineers and scientists who are not only proficient in AI technologies but are also thoughtful about their ethical development and deployment. As educators and authors, our greatest reward would be to see students use the knowledge gained from this book to create innovative solutions that address real-world challenges and contribute positively to society.