Transfer Learning

This book provides a comprehensive, yet accessible, entry point into this transformative field. It is meticulously crafted to serve as the primary textbook for undergraduate (B.Tech) and postgraduate (M.Tech) courses in Artificial Intelligence, Machine Learning, and Data Science. It bridges the gap between dense academic papers and superficial blog posts by focusing on building deep, intuitive understanding backed by practical, hands-on examples.

Key Features:

1. Lucid and Simple Language: Complex topics are broken down into easy-to-digest explanations, making the book accessible to students from various engineering backgrounds.
2. Practical Code Examples: Every major concept is accompanied by code snippets, demonstrating how to implement the techniques using popular, industry-standard libraries.
3. Intuition-First Approach: We use visual aids, flowcharts, and relatable analogies to build strong intuition, which is crucial for effective problem-solving.
4. Structured Learning Path: The 10-chapter structure provides a logical journey from fundamentals to advanced frontiers, making it ideal for a semester-long course.
5. Real-World Case Studies: The book explores impactful applications, from diagnosing diseases with medical scans to building intelligent chatbots, showing the real-world relevance of the material.
6. Future-Ready Content: Includes up-to-date coverage of the latest advancements, such as Transformer models, Foundation Models, and Self-Supervised Learning, ensuring students are learning current and future-proof skills.
7. End-of-Chapter Resources: Each chapter concludes with a concise summary, a set of review questions (both theoretical and practical), and a list of references for further reading.


Who Should Read This Book?


1. B.Tech/B.E. Students in Computer Science, Information Technology, and AI/ML.
2. M.Tech/M.E. Students specializing in AI, Data Science, and Machine Learning.
3. Software Developers and Practitioners looking to integrate powerful AI capabilities into their applications without starting from scratch.
4. Self-Taught AI Enthusiasts who want a structured, comprehensive, and practical guide to one of the most important topics in modern AI.


This book empowers you to stand on the shoulders of giants, leveraging vast, pre-existing knowledge to build intelligent systems faster, better, and with less data.