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paperback. Zustand: Very Good. Shipped within 24 hours from our UK warehouse. Clean, undamaged book with no damage to pages and minimal wear to the cover. Spine still tight, in very good condition. Remember if you are not happy, you are covered by our 100% money back guarantee.

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Paperback. Zustand: Brand New. 225 pages. 8.75x7.25x0.75 inches. In Stock.

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xviii, 325 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.

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Zustand: Gut. XIV, 302 pages Ex-Library book in good condition. 9783540069690 Sprache: Englisch Gewicht in Gramm: 550.

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Zustand: New. &Uumlber den AutorASHISH AGRAWAL is a qualified technocrat, offering two decades of multifaceted experience as a technology leader, trusted advisor, and Enterprise Cloud Architect (Infra, Apps, Data, and Security). He drives a profou.

Paperback. Zustand: Brand New. 102 pages. 9.25x6.10x0.22 inches. In Stock.

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and practice, when they are confronted with the huge size of the underlying data. Solutions to this problem are aptly covered in the book.In addition, the book covers a wide range of representation techniques that are important for academics and ML practitioners alike, such as Locality Sensitive Hashing (LSH), Distance Metrics and Fractional Norms, Principal Components (PCs), Random Projections and Autoencoders. Several experimental results are provided in the book to demonstrate the discussed techniques' effectiveness.…

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Paperback. Zustand: Brand New. 190 pages. 8.25x0.43x11.00 inches. In Stock.

Zustand: New. 2026. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.

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Zustand: New. Lalit Prasad is working as a Director (I/C) at Dr D Y Patil Institute of Management Studies (DYPIMS), Pune of Savitribai Phule Pune University. He is an approved PhD research guide of Savitribai Phule Pune University. He is an alumnus of IIM, Indo.

Taschenbuch. Zustand: Neu. Neuware - Sustainable Entrepreneurship and Startup Ecosystem: Global Challenges and Opportunities explores the evolving intersection of sustainability, innovation, and entrepreneurship within a rapidly transforming global business environment.This edited volume comprises 39 rigorously reviewed research papers that examine critical dimensions of sustainable entrepreneurship and startup development. The contributions address themes such as green innovation, ESG integration, circular economy models, sustainable business strategies, digital transformation in startups, entrepreneurial finance, policy frameworks, and global market challenges. The chapters highlight emerging opportunities for startups in addressing climate change, social inequality, and resource optimization while maintaining economic viability. Empirical studies, conceptual frameworks, and case-based analyses provide insights into how entrepreneurial ecosystems can foster resilience, competitiveness, and long-term value creation. Collectively, the book offers scholarly and practical perspectives on building sustainable enterprises in both emerging and developed economies.This book is intended for students, faculty members, research scholars, corporate professionals, policymakers, and entrepreneurs engaged in management and business studies. It serves as a valuable reference for those interested in sustainability-driven innovation, startup ecosystems, and global entrepreneurial challenges.…

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and practice, when they are confronted with the huge size of the underlying data. Solutions to this problem are aptly covered in the book.In addition, the book covers a wide range of representation techniques that are important for academics and ML practitioners alike, such as Locality Sensitive Hashing (LSH), Distance Metrics and Fractional Norms, Principal Components (PCs), Random Projections and Autoencoders. Several experimental results are provided in the book to demonstrate the discussed techniques¿ effectiveness.…

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