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About Parag Kulkarni
Parag is a marathon runner, Tedx speaker, husband of a bright doctor and above all a dreamer. He loves to write poetry and articulate his creative and innovative thoughts and deliver them through his passionate talks. He is also an entrepreneur, Machine Learning researcher and author of best-selling Innovation Strategy, ML and Data science books. An avid reader, Parag holds PhD from IIT Kharagpur (2001), management education from IIM Kolkata and was conferred higher doctorate DSc by UGSM monarch, Switzerland (2010). He is the first higher doctorate in the area of knowledge innovation. Parag’s machine learning ideas resulted in pioneering products and have become commercially successful and produced unprecedented impact. He delivered over one thousand keynote addresses and 200+ tutorials across the globe. His work on Systemic Machine Learning published by IEEE is widely cited. Over 1000 institutes and 10,00,000+ professionals benefitted from Parag’s talks, research and systemic consultations. Parag helped underperforming professionals and students to transform into happy and passionate warriors. Fellow of the IET, IETE, and senior member IEEE, Parag is recipient of Oriental Foundation Scholarship and was nominated for prestigious Bhatnagar award in 2013 and 2014. He was also awarded IETE-KR Phadke award for innovative entrepreneurship and research in 2019.
Parag has published over 300+ research papers in peer reviewed journals and conferences: (https://scholar.google.co.in/citations?user=dvi_iwEAAAAJ&hl=en),
He invented over a dozen patents and authored 14 books (with publishers like Bloomsbury, IEEE, Wiley, Prentice Hall, Oxford University Press, etc.): (https://www.amazon.in/Parag-Kulkarni/e/B002U66T7K). His book YD – YearDown portrays interesting perspective on education and was adapted for TV serial by Sony Marathi by well-known movie director Sameer Patil. His poem collection was specially appreciated by poet late Mangesh Padgaonkar. Parag’s book “Knowledge Innovation Strategy” was listed as a game changing business book by Hindustan Times. It has foreword and endorsement with special acclamation by Dr. FC Kohli and Ratan Tata. Parag was the first PhD guide in Computer Engineering at COEP, Pune and has guided 20 PhD candidates. He has over 25 years of experience in technologies, product building and applications of AI and ML to different verticals. In the past, he headed research divisions of many companies including Siemens, IDeaS, Capsilon, etc. As an AI consultant he helped to build game changing products for companies Envestent, TechMahindra, UST Global, Agrisk etc. He founded startups iKnowlation – India, Kvinna New Zealand and created social value through innovation and research. Parag is a prolific speaker and is associated with many technical and B-schools of repute like IITs, IIMs, Tokyo Int. University Japan and Masaryk University, Brno – Czech Republic etc. He is a pioneer of concepts of Systemic Machine Learning, Reverse Hypothesis Machine Learning, Context Vector Machines and Deep Explorative Machine Learning. He has helped as AI and ML consultant and innovation strategist to over two dozen organizations in Singapore, US, Japan and India. He worked on social good and developed over dozen products for in health care and education with focus on creating value at Bottom of Pyramid.
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Books By Parag Kulkarni
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'Strategy' and 'innovation' are terms that have increasingly lost their meaning in today's cut-throat business environment. This book gives these words a fresh meaning to advocate new pathways for change, showing us how to turn grave adversities into lifetime opportunities. Knowledge Ocean Strategy shows us how companies like Aquachill, AirTight Networks, Serum Institutes, Mapro, Ketan Food Exports, PARI, Tata Group, Chitale Dairies and Aditya Auto Test could find simple, refreshing solutions to complex problems to create their own uncontested knowledge space. In this seminal book, innovation strategist and knowledge innovation expert, Parag Kulkarni challenges competition-based strategies and those based on a mere 'more for less' paradigm using classic examples to unfold effective strategies based on associative knowledge building.
In the midst of fierce competition and a turbulent market, Knowledge Ocean Strategy presents an important breakthrough in innovation and strategic business thinking and will be a great motivator for organisations that aim to expand knowledge boundaries beyond competitive landscape.
It will also help making the transition from competition- to knowledge- centric; analysis- to synthesis-centric and isolation- to association-centric organization building; a systematic approach for a big leap and knowledge advantage.
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The book is designed for the senior level undergraduate, and postgraduate students of computer science and engineering.
� Contains numerous examples and case studies.
� Discusses Apache�s Hadoop�a software framework that enables distributed processing of large datasets across the clusters of computing machines.
� Incorporates review questions, MCQs, laboratory assignments and critical thinking questions at the end of the chapters, wherever required.
This book presents thoughts and pathways to build revolutionary machine learning models with the new paradigm of machine learning to adapt behaviorism. It focuses on two aspects – one focuses on architecting a choice process to lead users on the certain choice path while the second focuses on developing machine learning models based on choice paradigm. This book is divided in three parts where part one deals with human choice and choice architecting models with stories of choice architects. Second part closely studies human choosing models and deliberates on developing machine learning models based on the human choice paradigm. Third part takes you further to look at machine learning based choice architecture. The proposed pioneering choice-based paradigm for machine learning presented in the book will help readers to develop products – help readers to solve problems in a more humanish way and to negotiate with uncertainty in a more graceful but in an objective way. It will help to create unprecedented value for business and society. Further, it will unveil a new paradigm for modern intelligent businesses to embark on the new journey; the journey of transition from shackled feature rich and choice poor systems to feature flexible and choice rich natural behaviors.
This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same—the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as well as machine intelligence enthusiasts. It can also used by practitioners to develop new machine learning applications to solve problems that require creativity.
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There are always difficulties in making machines that learn from experience. Complete information is not always available—or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm—creating new learning applications and, ultimately, more intelligent machines.
The first book of its kind in this new and growing field, Reinforcement and Systemic Machine Learning for Decision Making focuses on the specialized research area of machine learning and systemic machine learning. It addresses reinforcement learning and its applications, incremental machine learning, repetitive failure-correction mechanisms, and multiperspective decision making.
- Introduction to Reinforcement and Systemic Machine Learning
- Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning
- Systemic Machine Learning and Model
- Inference and Information Integration
- Adaptive Learning
- Incremental Learning and Knowledge Representation
- Knowledge Augmentation: A Machine Learning Perspective
- Building a Learning System With the potential of this paradigm to become one of the more utilized in its field, professionals in the area of machine and systemic learning will find this book to be a valuable resource.